The DOI matches its Crossref record; 117 of the 130 references it cites were found in a registry or an index; 30 of the 45 sources its claims cite were checked for support.
Federated Learning for Healthcare Informatics
Open accessAbstract
With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies, and pharmaceutical industries, among others. This access provides an unprecedented opportunity for data science technologies to derive data-driven insights and improve the quality of care delivery. Healthcare data, however, are usually fragmented and private making it difficult to generate robust results across populations. For example, different hospitals own the electronic health records (EHR) of different patient populations and these records are difficult to share across hospitals because of their sensitive nature. This creates a big barrier for developing effective analytical approaches that are generalizable, which need diverse, "big data." Federated learning, a mechanism of training a shared global model with a central server while keeping all the sensitive data in local institutions where the data belong, provides great promise to connect the fragmented healthcare data sources with privacy-preservation. The goal of this survey is to provide a review for federated learning technologies, particularly within the biomedical space. In particular, we summarize the general solutions to the statistical challenges, system challenges, and privacy issues in federated learning, and point out the implications and potentials in healthcare.
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Claims the paper makes
14 No basis given · 10 Own interpretation · 9 Supported · 8 Partly supported · 3 Sources not found · 3 No full text of the sources · 1 Couldn’t confirm · 1 Not checked yet · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Another commonly used method is globally sharing a small portion of data between all the clients [75, 111]. The shared subset is required containing a uniform distribution over classes from the central server to the clients.”
§ Federated Learning for Healthcare Informatics > Federated Learning > Statistical Challenges of Federated Learning
What the sources say
- Could not verifycites [75] Nishio T, Yonetani R (2018) Client selection for federated learning with heterogeneous resources in mobile edge. arXiv:1804.08333 sourceExperimental — accuracy not yet measured
The Experimental Setup (p. 5) describes IID and non-IID data assignments, but not sharing a small, class-balanced subset among clients. The Results (p. 7) discuss increasing client participation or using model compression to alleviate non-IID effects. Searches for shared data and uniform class distribution found no passage addressing the claimed method. (LLM, conf=0.76)
- Supportedcites [111] Zhao Y, Li M, Lai L, Suda N, Civin D, Chandra V (2018) Federated learning with non-iid data. arXiv:1806.00582 source
Section 4.2 (p. 7) proposes a small, globally shared dataset with a uniform distribution over classes, distributed from the cloud to clients to improve federated learning with non-IID data. Section 4.2 (p. 8) reports improved accuracy using this strategy. (LLM, conf=0.93)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Could not verify
Every source the paper cites for this claim was checked and supports it.
In the paper
“When the component distributions are actually different, pluralism can outperform the “ideal” IID baseline.”
§ Federated Learning for Healthcare Informatics > Federated Learning > Statistical Challenges of Federated Learning
What the sources say
- Supportedcites [28] Eichner H, Koren T, McMahan HB, Srebro N, Talwar K (2019) Semi-cyclic stochastic gradient descent. arXiv:1904.10120 source
The Summary (p. 8) states the claim directly, adding that the paper's experiments illustrate it. Section 4 (p. 4) explains that separate predictors can benefit when component subproblems conflict. (LLM, conf=0.95)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“It is demonstrated can work with certain non independent identical distribution (non-IID) data by requiring all the clients to share the same model.”
§ Federated Learning for Healthcare Informatics > Federated Learning > Statistical Challenges of Federated Learning
What the sources say
- Supportedcites [69] McMahan B, Moore E, Ramage D, Hampson S, Arcas BA (2017) Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics, pp 1273–1282 source
The Experimental Results (p. 6) report that FedAvg achieved substantial speedups on pathologically non-IID MNIST data and worked on non-IID Shakespeare data. The FederatedAveraging Algorithm (p. 4) describes clients updating a shared starting model and the server averaging their models. The paper does not establish that sharing a model alone is sufficient for every non-IID setting, but the claim is limited to certain data. (LLM, conf=0.88)
boostedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Most centralized models are trained on the aggregated training samples obtained from the samples drawn from the local clients [96, 111]. Intrinsically, the centralized model is trained to minimize the loss with respect to the uniform distribution [73]”
§ Federated Learning for Healthcare Informatics > Federated Learning > Statistical Challenges of Federated Learning
What the sources say
- Not matchedcites [96] Smith V, Chiang CK, Sanjabi M, Talwalkar AS (2017) Federated multi-task learning. In: Advances in neural information processing systems, pp 4424–4434
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Partly supportedcites [111] Zhao Y, Li M, Lai L, Suda N, Civin D, Chandra V (2018) Federated learning with non-iid data. arXiv:1806.00582 sourceExperimental — accuracy not yet measured
Section 3.1 (p. 4) describes centralized training as optimizing population loss, but it does not say the population distribution is uniform. Sections 4.1–4.2 (p. 7) specify a uniform distribution over classes only for a separate, globally shared dataset used in the authors’ data-sharing strategy. That does not establish that centralized models generally minimize loss with respect to a uniform distribution. (LLM, conf=0.84)
- Not matchedcites [73] Mohri M, Sivek G, Suresh AT (2019) Agnostic federated learning. In: Chaudhuri K, Salakhutdinov R (eds) Proceedings of the 36th International conference on machine learning, proceedings of machine learning research, vol 97. PMLR, Long Beach, pp 4615–4625
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
Every source the paper cites for this claim was checked and supports it.
In the paper
“The performance of convolutional neural networks trained with FedAvg algorithm can reduce significantly due to the weight divergence [111].”
§ Federated Learning for Healthcare Informatics > Federated Learning > Statistical Challenges of Federated Learning
What the sources say
- Supportedcites [111] Zhao Y, Li M, Lai L, Suda N, Civin D, Chandra V (2018) Federated learning with non-iid data. arXiv:1806.00582 source
The Introduction (p. 1) reports substantial accuracy reductions for CNNs trained with FedAvg on highly skewed non-IID data. The Introduction (p. 2) attributes that reduction to weight divergence, directly supporting the claim's explanation. (LLM, conf=0.94)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“For example, Obermeyer et al. [76] found that a commonly used algorithm to determine enrollment in specific health programs was biased against African Americans, assigning the same level of risk to healthier Caucasian patients.”
§ Federated Learning for Healthcare Informatics > Applications > Healthcare
What the sources say
- Supportedcites [76] Obermeyer Z, Powers B, Vogeli C, Mullainathan S (2019) Dissecting racial bias in an algorithm used to manage the health of populations. Science 366(6464):447–453 source
The paper’s opening section says the widely used algorithm helps identify patients for additional care and that, at a given risk score, Black patients are considerably sicker than White patients. The “Health disparities conditional on risk score” section likewise reports that Black patients are less healthy than White patients at any level of algorithm prediction. Page numbers are not provided in our copy. (LLM, conf=0.94)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Thus, some researchers combine DP with SMC to reduce the growth of noise injection as the number of parties increases without sacrificing privacy while preserving provable privacy guarantees, protecting against extraction attacks and collusion threats [18, 100].”
§ Federated Learning for Healthcare Informatics > Federated Learning > Privacy and Security
What the sources say
- Could not verifycites [18] Cheng K, Fan T, Jin Y, Liu Y, Chen T, Yang Q (2019) Secureboost: a lossless federated learning framework. arXiv:1901.08755 sourceExperimental — accuracy not yet measured
The Introduction (p. 1) describes noise injection in differential privacy, and the Security Discussion (p. 5) mentions secure multi-party computation as a way to limit information leakage. Neither passage says the methods are combined or that combining them reduces noise growth as the number of parties increases. Searches for that combination, noise growth, and party count found no supporting passage. (LLM, conf=0.76)
- Not checked — no full text of the sourcecites [100] Truex S, Baracaldo N, Anwar A, Steinke T, Ludwig H, Zhang R (2018) A hybrid approach to privacy-preserving federated learning. arXiv:1812.03224 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Could not verify
Every source the paper cites for this claim was checked and supports it.
In the paper
“Therefore, federated learning has a promising application in NLP like virtual keyboard prediction [7, 39, 70].”
§ Federated Learning for Healthcare Informatics > Applications > Others
What the sources say
- Supportedcites [7] Bonawitz K, Eichner H, Grieskamp W, Huba D, Ingerman A, Ivanov V, Kiddon C, Konecny J, Mazzocchi S, McMahan HB, et al. (2019) Towards federated learning at scale: System design. arXiv:1902.01046 source
The Applications section (p. 8) describes Gboard using federated learning for keyboard content suggestions and next-word prediction. It also reports that the federated next-word model outperformed comparison models in live experiments, supporting the characterization of keyboard prediction as a promising application. (LLM, conf=0.94)
- Supportedcites [39] Hard A, Rao K, Mathews R, Beaufays F, Augenstein S, Eichner H, Kiddon C, Ramage D (2018) Federated learning for mobile keyboard prediction. arXiv:1811.03604 source
The Abstract (p. 1) describes training a language model with federated learning for next-word prediction in a smartphone virtual keyboard and reports better prediction recall than server-based training. The Conclusion (p. 6) says the federated model outperformed the comparison models on that task, supporting the claim that this is a promising NLP application. (LLM, conf=0.94)
- Supportedcites [70] McMahan HB, Ramage D, Talwar K, Zhang L (2017) Learning differentially private recurrent language models. arXiv:1710.06963 source
The Introduction (p. 1) identifies next-word prediction on a mobile keyboard as the paper’s motivating application. The Experimental Results (p. 6) evaluate federated training of a language model tuned for that setting, and the Conclusions (p. 9) describe its promise for real-world applications. The experiments used a public dataset, so this supports the claim of a promising application, not a deployed keyboard system. (LLM, conf=0.92)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Their results show that training the model in the federated learning framework leads to comparable performance to the traditional centralized learning setting.”
§ Federated Learning for Healthcare Informatics > Applications > Healthcare
What the sources say
- Supportedcites [90] Sharma P, Shamout FE, Clifton DA (2019) Preserving patient privacy while training a predictive model of in-hospital mortality. arXiv:1912.00354 source
The Abstract (p. 1) states that federated training achieved performance comparable to traditional centralized training. The Problem Formulation (p. 2) identifies the task as predicting in-hospital mortality for ICU patients, and the Summary (p. 4) repeats the performance comparison. (LLM, conf=0.95)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, this specific uniform distribution is not an adequate solution in most scenarios.”
§ Federated Learning for Healthcare Informatics > Federated Learning > Statistical Challenges of Federated Learning
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 19
- Probably not a substantial claim. 13
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Privacy and Security: Additional privacy protections are needed for unreliable participating clients. It is impossible to ensure all clients are equally reliable.”
§ Federated Learning for Healthcare Informatics > Federated Learning
What the sources say
- Partly supportedcites [13] Caldas S, Wu P, Li T, Konečnỳ J, McMahan HB, Smith V, Talwalkar A (2018) Leaf: a benchmark for federated settings. arXiv:1812.01097 sourceExperimental — accuracy not yet measured
The Introduction (p. 1) says federated methods must address privacy and security concerns because personally generated data is sensitive. It does not say that participating clients are unreliable, that their reliability cannot be ensured, or that unreliability calls for additional privacy protections. (LLM, conf=0.83)
- Not matchedcites [96] Smith V, Chiang CK, Sanjabi M, Talwalkar AS (2017) Federated multi-task learning. In: Advances in neural information processing systems, pp 4424–4434
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: The number of participating clients in federated learning is large, potentially thousands or millions.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Federated dropout not only reduces the downlink communication but also reduces the size of uplink updates. Moreover, the local computational costs is correspondingly reduced since the local training procedure dealing with parameters with smaller dimensions.”
§ Federated Learning for Healthcare Informatics > Federated Learning > Communication Efficiency of Federated Learning
What the sources say
- Supportedcites [12] Caldas S, Konečny J, McMahan HB, Talwalkar A (2018) Expanding the reach of federated learning by reducing client resource requirements. arXiv:1812.07210 source
Section 3.2 (p. 4) says federated dropout transmits only the coefficients needed for a smaller sub-model, reducing server-to-client communication. It explicitly states that client-to-server updates are smaller and that local training requires fewer FLOPS. (LLM, conf=0.95)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Communication Efficiency: The number of clients K is large and can be much bigger than the average number of training sample stored in the activated clients, i.e., K ≫ (n/K).”
§ Federated Learning for Healthcare Informatics > Federated Learning
What the sources say
- Supportedcites [13] Caldas S, Wu P, Li T, Konečnỳ J, McMahan HB, Smith V, Talwalkar A (2018) Leaf: a benchmark for federated settings. arXiv:1812.01097 source
Table 1 (Section 2 LEAF, p. 3) reports thousands to millions of devices for several datasets, with far fewer mean samples per device—for example, 660,120 devices and 2.42 samples per device in Sent140. The Introduction (p. 1) also describes federated settings as having very large numbers of devices. These passages support the claim that client count can greatly exceed average samples per client, though not for every dataset listed. (LLM, conf=0.91)
- Not matchedcites [96] Smith V, Chiang CK, Sanjabi M, Talwalkar AS (2017) Federated multi-task learning. In: Advances in neural information processing systems, pp 4424–4434
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Therefore, some researches began to study fully decentralized framework where the central server is not required [41, 60, 85, 91]. The local clients are distributed over the graph/network where they only communicate with their one-hop neighbors.”
§ Federated Learning for Healthcare Informatics > Federated Learning > Communication Efficiency of Federated Learning
What the sources say
- Supportedcites [41] He C, Tan C, Tang H, Qiu S, Liu J (2019) Central server free federated learning over single-sided trust social networks. arXiv:1910.04956 source
The Introduction (p. 1) describes a decentralized architecture in which the central node is removed and each node communicates only with its neighbors. It also presents the paper’s federated-learning setting as one without a central server; graph neighbors correspond to the citing sentence’s one-hop neighbors. (LLM, conf=0.94)
- Supportedcites [60] Lalitha A, Kilinc OC, Javidi T, Koushanfar F (2019) Peer-to-peer federated learning on graphs. arXiv:1901.11173 source
The Introduction (p. 1) explicitly describes a fully decentralized framework with no centralized controller and nodes that communicate only with their one-hop neighbors. The Discussion (p. 6) likewise describes learning with no central server. (LLM, conf=0.95)
- Partly supportedcites [85] Roy AG, Siddiqui S, Pölsterl S, Navab N, Wachinger C (2019) Braintorrent: a peer-to-peer environment for decentralized federated learning. arXiv:1905.06731 sourceExperimental — accuracy not yet measured
The Introduction (p. 2) and Conclusion (p. 8) describe BrainTorrent as server-less, with clients communicating directly rather than through a central server. The one-hop-neighbor restriction is not supported as a general network rule: Section 2.2 (p. 4) says every client is directly connected to every other client, and an initiating client pings all the others. (LLM, conf=0.86)
- Partly supportedcites [91] Shayan M, Fung C, Yoon CJ, Beschastnikh I (2018) Biscotti: a ledger for private and secure peer-to-peer machine learning. arXiv:1811.09904 sourceExperimental — accuracy not yet measured
The Abstract (p. 1) describes Biscotti as a fully decentralized peer-to-peer approach, supporting the claim that it does not require a central server. The Noising protocol (p. 7) says a peer contacts selected noising peers, but our copy does not establish that clients communicate only with one-hop neighbors. (LLM, conf=0.87)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Compared to AFL, Li et al. [64] proposed q-Fair Federated Learning (q-FFL), assigning higher weight to devices with poor performance, so that the distribution of accuracy in the network reduces in variance.”
§ Federated Learning for Healthcare Informatics > Federated Learning > Statistical Challenges of Federated Learning
What the sources say
- Supportedcites [64] Li T, Sanjabi M, Smith V (2019) Fair resource allocation in federated learning. arXiv:1905.10497 source
The Introduction (p. 1) says q-FFL gives higher relative weight to devices with higher loss. Section 4.2 (p. 7) reports that q-FFL with q > 0 reduces the variance of testing accuracies across devices. (LLM, conf=0.94)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Statistical Challenge: The data distribution among all clients differ greatly, i.e., , we have . It is such that any data points available locally are far from being a representative sample of the overall distribution, i.e., .”
§ Federated Learning for Healthcare Informatics > Federated Learning
What the sources say
- Partly supportedcites [13] Caldas S, Wu P, Li T, Konečnỳ J, McMahan HB, Smith V, Talwalkar A (2018) Leaf: a benchmark for federated settings. arXiv:1812.01097 sourceExperimental — accuracy not yet measured
The Introduction (p. 1) says devices have different, though perhaps related, data-generating distributions, supporting the claim of heterogeneous client data. It does not establish that every client's local samples are far from representative of the overall distribution; that absolute formulation goes beyond the passage. (LLM, conf=0.84)
- Not matchedcites [96] Smith V, Chiang CK, Sanjabi M, Talwalkar AS (2017) Federated multi-task learning. In: Advances in neural information processing systems, pp 4424–4434
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“However, DP only protects users from data leakage to a certain extent and may reduce performance in prediction accuracy because it is a lossy method [18].”
§ Federated Learning for Healthcare Informatics > Federated Learning > Privacy and Security
What the sources say
- Supportedcites [18] Cheng K, Fan T, Jin Y, Liu Y, Chen T, Yang Q (2019) Secureboost: a lossless federated learning framework. arXiv:1901.08755 source
The Introduction (p. 1) says differential privacy prevents user-data leakage only to a certain degree. It also says that adding noise makes the method lossy in machine learning and may result in unsatisfactory prediction accuracy. (LLM, conf=0.95)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Federated learning has also enabled predictive modeling based on diverse sources, which can provide clinicians with additional insights into the risks and benefits of treating patients earlier [9, 10, 90].”
§ Federated Learning for Healthcare Informatics > Applications > Healthcare
What the sources say
- Partly supportedcites [9] Boughorbel S, Jarray F, Venugopal N, Moosa S, Elhadi H, Makhlouf M (2019) Federated uncertainty-aware learning for distributed hospital ehr data. arXiv:1910.12191 sourceExperimental — accuracy not yet measured
The Introduction (p. 1) says federated learning enables predictive models trained jointly from different EHR sources. The Clinical Application section (p. 2) describes predicting preterm-birth risk three months before delivery, but does not say the model gives clinicians insight into the risks and benefits of treating patients earlier. (LLM, conf=0.88)
- Partly supportedcites [10] Brisimi TS, Chen R, Mela T, Olshevsky A, Paschalidis IC, Shi W (2018) Federated learning of predictive models from federated electronic health records. Int J Med Inform 112:59–67 sourceExperimental — accuracy not yet measured
The Abstract (Objective) and Conclusions describe a federated model that uses distributed EHR data to predict future hospitalizations. The Introduction (Health application) links prediction and early prevention, but the paper does not assess the risks and benefits of treating patients earlier or show that its model gives clinicians insight into that trade-off. Page numbers are unavailable in our copy. (LLM, conf=0.86)
- Partly supportedcites [90] Sharma P, Shamout FE, Clifton DA (2019) Preserving patient privacy while training a predictive model of in-hospital mortality. arXiv:1912.00354 sourceExperimental — accuracy not yet measured
The Introduction (p. 1) describes federated training of an in-hospital mortality model using data distributed across hospitals, and the Summary (p. 4) discusses the potential value of larger, more diverse datasets. The paper does not say that its model gives clinicians insight into the risks and benefits of treating patients earlier. Its evaluation used one database distributed among virtual workers, rather than data collected from multiple hospitals (Dataset, p. 3). (LLM, conf=0.86)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Federated learning is a viable method to connect EHR data from medical institutions, allowing them to share their experiences, and not their data, with a guarantee of privacy [9, 25, 34, 45, 65, 82].”
§ Federated Learning for Healthcare Informatics > Applications > Healthcare
What the sources say
- Partly supportedcites [9] Boughorbel S, Jarray F, Venugopal N, Moosa S, Elhadi H, Makhlouf M (2019) Federated uncertainty-aware learning for distributed hospital ehr data. arXiv:1910.12191 sourceExperimental — accuracy not yet measured
The Introduction (p. 1) says federated learning jointly trains models from different EHR sources without sharing data, and §3.2 (p. 3) says the proposed algorithm preserves data privacy by sending model updates to the data location. Those passages support the paraphrase, but not the citing sentence’s stronger claim of a privacy guarantee; the Introduction also identifies privacy as an ongoing challenge. (LLM, conf=0.82)
- Partly supportedcites [25] Duan R, Boland MR, Liu Z, Liu Y, Chang HH, Xu H, Chu H, Schmid CH, Forrest CB, Holmes JH, et al. (2020) Learning from electronic health records across multiple sites: a communication-efficient and privacy-preserving distributed algorithm. J Am Med Inform Assoc 27(3):376–385 sourceExperimental — accuracy not yet measured
The Conclusion (page not provided) says ODAL enables analysis across multiple clinical datasets without transferring patient-level data, supporting the claim that institutions can learn from EHR data without sharing those records. It defines privacy preservation in that limited sense; it does not establish the citing sentence’s broader “guarantee of privacy.” (LLM, conf=0.87)
- Partly supportedcites [34] Gruendner J, Schwachhofer T, Sippl P, Wolf N, Erpenbeck M, Gulden C, Kapsner LA, Zierk J, Mate S, Stürzl M, et al. (2019) KETOS: Clinical decision support and machine learning as a service–A training and deployment platform based on Docker, OMOP-CDM, and FHIR Web Services. PloS one 14(10):1–16 sourceExperimental — accuracy not yet measured
The Results section (Example model 3; page unavailable) describes training models across a federated hospital network while only aggregated statistics leave each institution. The Discussion likewise says patient data need not leave hospitals. These passages support privacy-preserving cross-institutional training, but they do not establish the claimed guarantee of privacy. (LLM, conf=0.86)
- Partly supportedcites [45] Huang L, Liu D (2019) Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records. arXiv:1903.09296 sourceExperimental — accuracy not yet measured
The Introduction (page unavailable) says federated learning keeps EMR data local and aggregates locally computed results, allowing institutions to learn together without sharing the records. The Conclusions (page unavailable) describe the authors’ clustering method as avoiding a privacy breach. Neither passage establishes the citing sentence’s stronger claim of a guarantee of privacy. (LLM, conf=0.84)
- Partly supportedcites [65] Li Z, Roberts K, Jiang X, Long Q (2019) Distributed learning from multiple ehr databases: Contextual embedding models for medical events. J Biomed Inform 92:103138 sourceExperimental — accuracy not yet measured
The Distributed Noise Contrastive Estimation and Privacy Protection sections describe building a model from multiple EHR databases without sharing patient data; sites share a differentially private count table instead. But the Conclusion and Discussion says the experiment used two EHR systems at one institution, not different hospitals, and calls for evaluation across hospitals. The paper describes privacy protection, not an unqualified guarantee of privacy. (LLM, conf=0.86)
- Not checked — no full text of the sourcecites [82] Raja PV, Sivasankar E (2014) Modern framework for distributed healthcare data analytics based on hadoop. In: Information and communication technology-EurAsia conference. Springer, pp 348–355 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Federated learning, a mechanism of training a shared global model with a central server while keeping all the sensitive data in local institutions where the data belong, provides great promise to connect the fragmented healthcare data sources with privacy-preservation.”
§ Federated Learning for Healthcare Informatics > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Mohri et al. [73] proposed a minimax optimization scheme, i.e., agnostic federated learning (AFL), where the centralized model is optimized for any possible target distribution formed by a mixture of the client distributions.”
§ Federated Learning for Healthcare Informatics > Federated Learning > Statistical Challenges of Federated Learning
What the sources say
- Not matchedcites [73] Mohri M, Sivek G, Suresh AT (2019) Agnostic federated learning. In: Chaudhuri K, Salakhutdinov R (eds) Proceedings of the 36th International conference on machine learning, proceedings of machine learning research, vol 97. PMLR, Long Beach, pp 4615–4625
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Apparently, we can consider three ways to reduce the communication cost: (a) reduce the number of clients K, (b) reduce the update size, (c) reduce the number of updates U.”
§ Federated Learning for Healthcare Informatics > Federated Learning > Communication Efficiency of Federated Learning
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Healthcare data, however, are usually fragmented and private making it difficult to generate robust results across populations.”
§ Federated Learning for Healthcare Informatics > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Federated learning holds great promises on healthcare data analytics.”
§ Federated Learning for Healthcare Informatics > Introduction
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Federated learning, a mechanism of training a shared global model with a central server while keeping all the sensitive data in local institutions where the data belong”
§ Federated Learning for Healthcare Informatics > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“Without well-designed incentives, self-interested mobile or other wearable devices will be reluctant to participate in federal learning tasks, which will hinder the adoption of federated learning [53].”
§ Federated Learning for Healthcare Informatics > Conclusions and Open Questions
What the sources say
- Not checked — no full text of the sourcecites [53] Kang J, Xiong Z, Niyato D, Yu H, Liang YC, Kim DI (2019) Incentive design for efficient federated learning in mobile networks: A contract theory approach. arXiv:1905.07479 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Federated tries to make isolated institutions or devices share their experiences, and the performance of machine learning model will be significantly improved by the formed large medical dataset.”
§ Federated Learning for Healthcare Informatics > Conclusions and Open Questions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Differential privacy (DP) [26] is an alternative theoretical model for protecting the privacy of individual data, which has been widely applied to many areas, not only traditional algorithms, e.g., boosting [27], principal component analysis [15], support vector machine [86], but also deep learning research [1, 70]. It ensures that the addition or removal does not substantially affect the outcome”
§ Federated Learning for Healthcare Informatics > Federated Learning > Privacy and Security
What the sources say
- Not checked — no full text of the sourcecites [26] Dwork C, Kenthapadi K, McSherry F, Mironov I, Naor M (2006) Our data, ourselves: Privacy via distributed noise generation. In: Annual international conference on the theory and applications of cryptographic techniques. Springer, pp 486–503 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, most health systems suffer from data clutter and efficiency problems.”
§ Federated Learning for Healthcare Informatics > Conclusions and Open Questions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This widespread adoption is due in part by the fact that it enables a collaborative modeling mechanism that allows for efficient ML all while ensuring data privacy and legal compliance between multiple parties or multiple computing nodes.”
§ Federated Learning for Healthcare Informatics > Applications
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The analyzed results are apparently worthless when dirty data are accidentally used as samples.”
§ Federated Learning for Healthcare Informatics > Conclusions and Open Questions
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Medical devices in hospitals have incomparable advantages over wearable devices.”
§ Federated Learning for Healthcare Informatics > Conclusions and Open Questions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Although SMC guarantees that none of the parties shares anything with each other or with any third party, it can not prevent an adversary from learning some individual information”
§ Federated Learning for Healthcare Informatics > Federated Learning > Privacy and Security
What the sources say
- Not checked — no full text of the sourcecites [8] Bonawitz K, Ivanov V, Kreuter B, Marcedone A, McMahan HB, Patel S, Ramage D, Segal A, Seth K (2017) Practical secure aggregation for privacy-preserving machine learning. In: Proceedings of the 2017 ACM SIGSAC conference on computer and communications security. ACM, pp 1175–1191 source
- Not matchedcites [78] Pathak M, Rane S, Raj B (2010) Multiparty differential privacy via aggregation of locally trained classifiers. In: Advances in neural information processing systems, pp 1876–1884
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Federated learning has the potential to connect all the isolated medical institutions, hospitals, or devices to make them share their experiences with privacy guarantee.”
§ Federated Learning for Healthcare Informatics > Conclusions and Open Questions
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, the prediction task is currently restricted and relatively simple.”
§ Federated Learning for Healthcare Informatics > Conclusions and Open Questions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The quality of data collected from multiple sources is uneven and there is no uniform data standard.”
§ Federated Learning for Healthcare Informatics > Conclusions and Open Questions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“The creation and utility of standardized common data models, such as OMOP [44], allow for more wide-spread replication analyses but it does not overcome the limitations of joint data access.”
§ Federated Learning for Healthcare Informatics > Applications > Healthcare
What the sources say
- Not matchedcites [44] Hripcsak G, Duke JD, Shah NH, Reich CG, Huser V, Schuemie MJ, Suchard MA, Park RW, Wong ICK, Rijnbeek PR, et al. (2015) Observational health data sciences and informatics (ohdsi): opportunities for observational researchers. Stud Health Technol Inform 216:574
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
The authors state this as an assumption.
The number of participating clients in federated learning is large, potentially thousands or millions.
“In federated learning, we usually assume the number of participated clients (e.g., phones, cars, clinical institutions...) is large, potentially in the thousands or millions.”
The authors state this as an assumption.
There are K activated clients where data reside, each with a data distribution and a number of samples.
“Mathematically, assume there are K activated clients where the data reside in (a client could be a mobile phone, a wearable device, or a clinical institution data warehouse, etc.). Let denote the data distribution associated with client k and nk the number of samples available from that client.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Due to the contemporary features, DFL can be integrated with P2Pbased communication technologies such as blockchain [37] to build decentralized FL systems.”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [37]
Experimental — accuracy not yet measuredIn the citing paper
“FL enables healthcare records that are located across different institutions to be connected without revealing personal information [11].”
From A comprehensive survey on federated learning techniques for healthcare informatics (2023) · cites this paper as [11]
In the citing paper
“Xu et al. [11] surveyed the papers that focus FL in the biomedical area to provide a review.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [11]
In the citing paper
“By enabling multiple institutions to collaborate on AI model development without exchanging patient data, FL addresses the most pressing concerns in healthcare AI: privacy and data integrity.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“By enabling multiple institutions to collaborate on AI model development without exchanging patient data, FL addresses the most pressing concerns in healthcare AI: privacy and data integrity.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“As Ref. [19] explains, only the model parameters or gradients-essentially the learned features or updates to the model-are shared with a central server or among participants.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“This ensures that the raw patient data, which may contain personally identifiable information, never leaves the institution's premises where it was collected.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“The authors of [19] highlight the statistical variance in data distribution among clients, communication efficiency, and the potential for malicious clients as operational challenges in FL.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“The core principle of DP is to ensure that the presence or absence of any individual's data do not substantially alter the outcome of an analysis.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“This method is advantageous for sharing insights from health data, such as statistical summaries or AI model parameters, while safeguarding patient confidentiality.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“It functions by introducing a calculated amount of random noise, either to the data itself or to the outputs of algorithms, thereby making it statistically challenging for attackers to deduce sensitive information about any individual in the dataset.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“By creating a secure and tamper-proof ledger for medical data transactions, blockchain technology ensures the integrity and traceability of patient data, which is paramount in clinical settings.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“As privacy protection intensifies with increased noise, the usefulness of the data for research and the accuracy of AI models may be compromised.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“While it excels at enhancing privacy, it can simultaneously lead to reduced accuracy of predictions or analyses due to the added noise.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“For instance, blockchain can be used to securely manage electronic health records (EHRs), providing a reliable source of patient data for healthcare professionals and AI algorithms while preventing unauthorized access and data breaches [19].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“Blockchain technology stands out for its unique attributes of decentralization, immutability, and transparency, which are particularly beneficial in the context of healthcare AI systems.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“This hybrid approach is designed to control the escalation of noise with the number of participants, thus maintaining both privacy and the practical value of the data.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“DP's application extends to various algorithms, including but not limited to boosting, principal component analysis, support vector machines, and deep learning.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“It is also effectively employed in Federated Learning scenarios involving decentralized data processing across multiple entities, such as hospitals or research institutions, to prevent indirect data leakage-a prevalent issue in collaborative environments [19].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“By enabling the training of AI models on decentralized data sources, FL ensures that sensitive patient information remains within the confines of the local institution, thereby preserving privacy.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“To counterbalance this, researchers often integrate DP with other methods like Secure Multi-party Computation (SMC) to balance privacy and data utility [19].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“At the same time, client selection protocols, model compression, and update reduction strategies aim to improve communication efficiency.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“FL's ability to integrate data from various sources without compromising privacy is a testament to its potential to enhance the capabilities of healthcare AI systems.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“Moreover, integrating Secure Multi-party Computation and Differential Privacy within FL frameworks enhances the privacy and security of the data, making FL a powerful tool for healthcare applications [19].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“Techniques like Agnostic Federated Learning and q-Fair Federated Learning have been proposed to address the issue of data heterogeneity.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“By adopting these technologies, healthcare providers can meet the requirements of laws such as HIPAA in the United States and GDPR in Europe, which mandate strict data privacy and security standards.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“The ethical considerations of patient data use are also addressed, as these technologies provide mechanisms to ensure that patient consent is obtained and that data are used to respect individual autonomy and dignity [19].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“In healthcare, AI performs a vital and crucial role in understanding the human thinking process through the computer using various effective AI-based algorithms to make complex examinations easier for healthcare professionals [83].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [83]
In the citing paper
“To overcome this issue, federated learning is used to train the shared global model using data from several institutions without compromising data privacy [13].”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [13]
In the citing paper
“However, the results of deep learning are less accurate, as the datasets for training and testing are smaller.”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [13]
In the citing paper
“It has been observed that addressing the security issues was primarily focused [11]; however, more attention needs to be paid to the protection of end-user privacy in modern electronic health care systems [12], [13].”
From Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey (2023) · cites this paper as [11]
In the citing paper
“Federated machine learning has shown promising results in the healthcare industry, as indicated in [146,147].”
From Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review (2023) · cites this paper as [146]
In the citing paper
“Xu et al. performed a survey focusing on the progress of federated learning in healthcare informatics [7].”
From Applications of Federated Learning; Taxonomy, Challenges, and Research Trends (2022) · cites this paper as [7]
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215 · cited 2×
“The naive way to solve the federated learning problem is through Federated Averaging (FedAvg) [61].”
BackgroundMethodDistilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204 · cited 1×
“, sparse, low-rank [58], or more specifically, pruning the least useful connections in a network [37, 113], weight quantization [17, 89], and model distillation [43].”
BackgroundMIMIC-III, a freely accessible critical care database
Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-W. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., & Mark, R. G. (2016) · Scientific Data · doi:10.1038/sdata.2016.35 · Cited by 8,141 · cited 1×
“tionbetweenparticipants aboutsomeoftheencryptedresults[70]. Table1:Summaryofrecentworkonfederatedlearningforhealthcare Problem MLMethod #Hospital Data PatientSimilarityLearning[54] Hashing 3 MIMIC-III[42] PatientSimilarityLearning[96] Hashing 20 MIMIC-III Phenotyping[47] *TF 1-5 MIMIC-III,UCSD[92] Phenotyping[59] NLP…”
MethodAdvances and Open Problems in Federated Learning
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.04977 · Cited by 7,857
Advances and Open Problems in Federated Learning
Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning · doi:10.1561/2200000083 · Cited by 7,772
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137 · cited 1×
“esearch[64].Whileprovidingahugeamountof patient data for analysis, EHRs contain systemic and random biases overall andspecifictohospitalsthatlimitthegeneralizabilityofresults.Forexample, Obermeyeretal.[68]foundthatacommonlyusedalgorithmtodetermineenrollment in specific health programs were biased against African America…”
BackgroundDeep Learning with Differential Privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978318 · Cited by 6,578 · cited 2×
“, boosting [27], principal component analysis [15], support vector machine [86], but also deep learning research [1, 70].”
BackgroundFederated Machine Learning: Concept and Applications
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322 · cited 3×
“privacyandlegalcompliancebetweenmultiplepartiesormultiplecomputing nodes. Some promising examples that highlight these capabilities are virtual keyboard prediction [31,62], smart retail [99], finance [97], vehicle-to-vehicle communication[80].Inthissection,wefocusprimarilyonapplicationswithin thehealthcarespace,butalso…”
BackgroundInfluentialHan, S., Mao, H., & Dally, W. J. (2015) · arXiv · doi:10.48550/arxiv.1510.00149 · Cited by 4,662 · cited 1×
“, sparse, low-rank [58], or more specifically, pruning the least useful connections in a network [37, 113], weight quantization [17, 89], and model distillation [43].”
Background
Research works printed but not linked: 5
- Kawa D, Punyani S, Nayak P, Karkera A, Jyotinagar V (2019) Credit risk assessment from combined bank records using federated learning. International Research Journal of Engineering and Technology (IRJET) 6(4):1355–1358
- Mohri M, Sivek G, Suresh AT (2019) Agnostic federated learning. In: Chaudhuri K, Salakhutdinov R (eds) Proceedings of the 36th International conference on machine learning, proceedings of machine learning research, vol 97. PMLR, Long Beach, pp 4615–4625
- Pathak M, Rane S, Raj B (2010) Multiparty differential privacy via aggregation of locally trained classifiers. In: Advances in neural information processing systems, pp 1876–1884
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Items 501–600 of 1,496
Machine Learning-Enhanced Attribute-Based Authentication for Secure IoT Access Control
Saleem, J., Raza, U., Hammoudeh, M. A. A., et al. (2025) · Sensors · doi:10.3390/s25092779 · Cited by 13
“Applications of Federated Learning include its use in mobile keyboards for predictive text [39], fraud detection systems in financial services [40] and personalized healthcare applications that train models on patient data across multiple hospitals while preserving privacy [41,42].”
A scoping review of semantic integration of health data and information
Zhang, H., Lyu, T., Yin, P., Bost, S. J., He, X., Guo, Y. M., Prosperi, M., Hogan, W. R., & Bian, J. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104834 · Cited by 17
Self-citationAddressing Heterogeneity in Federated Learning via Distributional Transformation
Yuan, H., Hui, B., Yang, Y., Burlina, P., Gong, N. Z., & Cao, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19839-7_11 · Cited by 14
“Existing federated learning (FL) studies focus on improving accuracy [35,50,43,39], convergence [12,6,37,17,45,32], communication cost [24,41,22,3,23,13,34,49], security and privacy [36,10,5,4], or others [16,20,11,47].”
BackgroundChinni, B. K., & Manlhiot, C. (2024) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2024.07.026 · Cited by 19
Federated learning of medical concepts embedding using BEHRT
Shoham, O. B., & Rappoport, N. (2024) · JAMIA Open · doi:10.1093/jamiaopen/ooae110 · Cited by 27
“Instead of LDA, HORDE model [22] used LSTM [23] for clinical notes, which is not bag-of-words.”
MethodRe-Identification in Differentially Private Incomplete Datasets
Sei, Y., Okumura, H., & Ohsuga, A. (2022) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2022.3175999 · Cited by 13
Privacy Issues, Attacks, Countermeasures and Open Problems in Federated Learning: A Survey
Guembe, B., Misra, S., & Azeta, A. A. (2024) · Applied Artificial Intelligence · doi:10.1080/08839514.2024.2410504 · Cited by 21
“Xu et al. (2021) introduced VerifyNet, a privacy-preserving technique that ensures safe gradient aggregation and verification.”
InfluentialArtificial Intelligence in Drug Discovery: Integrative Advances From Data to Therapeutic Innovation
Mehran, M. J., Mohammadzadeh, S., Bolideei, M., Barzigar, R., Haider, K. H., Jadgal, N., & Bahrami, Y. (2026) · Drug Development Research · doi:10.1002/ddr.70229 · Cited by 14
“Future strategies should emphasize the creation of standardized data‐ sharing frameworks, incorporating advanced technologies such as federated learning and blockchain to facilitate secure, collaborative research across global institutions (Xu et al. 2021; Haleem et al. 2021).”
Liu, T., Hu, X., Xu, H., Shu, T., & Nguyen, D. N. (2022) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2022.103309 · Cited by 12
Hierarchical federated learning with local model embedding
He, Y., Yan, D., & Chen, F. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106148 · Cited by 15
Atitallah, S. B., Driss, M., & Ghézala, H. B. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.10.326 · Cited by 14
Depriving the Survival Space of Adversaries Against Poisoned Gradients in Federated Learning
Lu, J., Hu, S., Wan, W., Li, M., Zhang, L. Y., Xue, L., & Jin, H. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3360869 · Cited by 15
“So far, FL has gained widespread adoption in a variety of real-world applications such as finance [4], medical care [40], and smart city [17], etc. FL is known to be vulnerable to poisoning attacks, wherein the adversary ( e.g. , compromised clients) can taint the local training data (referred to…”
BackgroundEdge-DPSDG: An Edge-Based Differential Privacy Protection Model for Smart Healthcare
Lyu, M., Ni, Z., Chen, Q., & Li, F. (2024) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2024.3366071 · Cited by 14
“Existing approaches to privacy protection under distributed smart healthcare include federal learning [25], [26], [27], [28], homomorphic encryption [29], [30], [31], secure multi-party computation [32], [33] and differential privacy [34], [35], [36], [37].”
Intrusion Detection and Prevention Techniques in FL Cloud-Based Healthcare 5.0
Vashishth, T. K., Sharma, V., Sharma, K. K., Kumar, B., Chaudhary, S., & Panwar, R. (2023) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-2639-8.ch010 · Cited by 12
Prompt-Based Personalized Federated Learning for Medical Visual Question Answering
Zhu, H., Togo, R., Ogawa, T., & Haseyama, M. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10445933 · Cited by 15
“In the general domain, the federated learning (FL) [9] and the personalized federated learning (pFL) [10] are proposed for training personalized models separately using private data.”
MethodPersonalized Federated Learning with Attention-Based Client Selection
Chen, Z., Li, J., & Shen, C. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447362 · Cited by 20
“The privacy-preserving solutions offered by federated learning have found extensive applications in domains such as healthcare, smart cities, and finance [3, 4, 5].”
BackgroundLightweight federated learning for STIs/HIV prediction
Nguyen, T. P. V., Yang, W., Tang, Z., Xia, X., Mullens, A. B., Dean, J. A., & Li, Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-56115-0 · Cited by 12
“There has been significant research on FL in the field of digital health 13–15 , 24– 26 .”
BackgroundMethodAlkhalifa, A. K., Alanazi, M. H., MAHMOOD, K., et al. (2024) · Fractals · doi:10.1142/s0218348x25400201 · Cited by 12
Austin, J. A., Lobo, E. H., Samadbeik, M., Engstrom, T., Philip, R., Pole, J. D., & Sullivan, C. M. (2024) · Journal of Medical Internet Research · doi:10.2196/58637 · Cited by 16
“Traditional centralized machine learning (ML) approaches face privacy and security risks [85] and limited predictive accuracy due to single-source data constraints [86].”
A Proactive Defense Against Model Poisoning Attacks in Federated Learning
Yan, H., Zheng, C., Chen, Q., Li, X., Wang, B., Li, H., & Lin, X. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3533029 · Cited by 14
“1109/TDSC.2025.3533029 from organizations like banks [2] and hospitals [3], governments like GDPR [4] and CPPA [5], and commercial companies like Google [6].”
InfluentialA Comprehensive Review of Open-Source Federated Learning Frameworks
Mehdi, M., Makkar, A., & Conway, M. E. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.03.232 · Cited by 12
“Federated learning o ff ers advantages such as improved model performance [4], reduced data transmission costs [5], and increased data privacy [6].”
Federated learning in food research
Fendor, Z., van der Velden, B. H. M., Wang, X., Carnoli, A. J., Mutlu, O., & Hürriyetoğlu, A. (2025) · Journal of Agriculture and Food Research · doi:10.1016/j.jafr.2025.102238 · Cited by 19
“…extensive reviews of FL applications have been published in other fields, like agriculture, smart cities, air pollution monitoring, and medicine (Ili´c & Ivanovi´c, 2023; Jiang, Kantarci, Oktug, & Soyata, 2020; Li, Fan, Tse, & Lin, 2020; Nguyen et al., 2022; Xu et al., 2021; Žalik & Žalik, 2023).).”
Edge Intelligence-based Privacy Protection Framework for IoT-based Smart Healthcare Systems
Akter, M., Moustafa, N., & Lynar, T. (2022) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps54753.2022.9798196 · Cited by 13
Rebirth of Distributed AI—A Review of eHealth Research
Khan, M. A., & Alkaabi, N. (2021) · Sensors · doi:10.3390/s21154999 · Cited by 13
“As far as we know, the existing surveys and reviews about FL in the healthcare sector are “Federated Learning for Healthcare Informatics” [23], which explains FL technology with a focus on Electronic Health Records (EHR) applications, and the second survey, “The future of digital health with federated learning” [24],…”
BackgroundFederated Markov Logic Network for indoor activity recognition in Internet of Things
Zhang, C., Ren, X., Zhu, T., Zhou, F., Liu, H., Lu, Q., & Ning, H. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109553 · Cited by 12
A Federated Learning-Based Light-Weight Privacy-Preserving Framework for Smart Healthcare Systems
Ramesh, V., Hariharasitaraman, S., Ganesh, S. S., B., P. N., & Hemalakshmi, G. R. (2022) · Advances in wireless technologies and telecommunication book series · doi:10.4018/978-1-7998-9636-4.ch019 · Cited by 11
Cao, C., Zhao, W., Guo, J., Wang, Z., Lü, A., & Liang, C. C. (2025) · The Innovation Medicine · doi:10.59717/j.xinn-med.2025.100154 · Cited by 14
Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare
Liu, C., Fan, H., Kim, M., Zhou, T., Yang, P., Zhao, L., Wang, Y., Che, Z., Liu, C., Li, B., et al. (2026) · Advanced Science · doi:10.1002/advs.202522900 · Cited by 20
“Com-plementarily, federated learning can support generalization by exposing models to heterogeneous institutional environments without centralized data aggregation [ 233 ].”
Securing Federated Learning With Blockchain in the Medical Field: Systematic Literature Review
Wang, X., Xie, Y., Chen, X., Yang, J., Li, R., Gao, W., Yan, Z., Zhou, H., & Ye, Z. (2026) · Journal of Medical Internet Research · doi:10.2196/79052 · Cited by 21
“This paradigm is well-suited for scenarios with strict privacy requirements and decentralized data that cannot be centrally stored, such as health care, finance, and smart cities [20-23].”
Privacy-preserving Blockchain-based Global Data Sharing for Federated Learning with Non-IID Data
Lian, Z., Zeng, Q., & Su, C. (2022) · IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW) · doi:10.1109/icdcsw56584.2022.00044 · Cited by 14
“Within the context of the medical industry, FL has been extensively studied as more and more healthcare data which is sensitive but with great value for research and analysis, is available from clinical institutions, medical centers, and patients [9].”
BackgroundSelf-citationAutoFL: Towards AutoML in a Federated Learning Context
Preuveneers, D. (2023) · Applied Sciences · doi:10.3390/app13148019 · Cited by 12
Tozzi, A. E., Croci, I., Voicu, I. P., Dotta, F., Colafati, G. S., Carai, A., Fabozzi, F., Lacanna, G., Premuselli, R., & Mastronuzzi, A. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1285775 · Cited by 11
“While compliance with regulations for data sharing is important for scienti fi c research in the fi eld of AI (26), technologies such as federated and swarm learning (27 – 29) and synthetic data (30) may be prioritized to accelerate the development of AI tools.”
BackgroundSung, G., & Park, E. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106068 · Cited by 12
PrivFR: Privacy-Enhanced Federated Recommendation With Shared Hash Embedding
Zhang, H., Zhou, X., Shen, Z., & Li, Y. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3387757 · Cited by 13
The Role of Data Quality for Reliable AI Performance in Medical Applications
Enshaei, N., & Naderkhani, F. (2024) · IEEE reliability magazine · doi:10.1109/mrl.2024.3430192 · Cited by 12
“By implementing federated learning, healthcare organizations can collaboratively improve AI models while adhering to strict privacy regulations and protecting sensitive patient information [5].”
BackgroundA privacy-preserving federated learning approach for airline upgrade optimization
Chen, S., & Huang, Y. (2024) · Journal of Air Transport Management · doi:10.1016/j.jairtraman.2024.102693 · Cited by 13
“The research of Xu et al. (2021) provides an overview of federated learning technology in the field of biomedicine.”
Leveraging towards Privacy-preserving using Federated Machine Learning for Healthcare Systems
Gandhi, N., Mishra, S., Bharti, S. K., & Bhagat, K. (2021) · 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) · doi:10.1109/conecct52877.2021.9622730 · Cited by 11
“A study was conducted on prediction of patient’s resistance for several types of treatments, medical conditions, drugs response and their corresponding chances of survival using a federated learning model by Xu et al [9].”
BackgroundMethodA tool for federated training of segmentation models on whole slide images
Lutnick, B. R., Manthey, D., Becker, J. U., Zuckerman, J. E., Rodrigues, L., Jen, K., & Sarder, P. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100101 · Cited by 12
An Improved Infrastructure for Privacy-Preserving Analysis of Patient Data
Smits, D., van Beusekom, B., Martin, F. J., et al. (2022) · Studies in health technology and informatics · doi:10.3233/shti220682 · Cited by 11
“frameworks that tackle this issue [1].”
Security and Privacy Concerns for Healthcare Wearable Devices and Emerging Alternative Approaches
Boumpa, E., Tsoukas, V., Gkogkidis, A., Spathoulas, G. P., & Kakarountas, A. P. (2022) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-06368-8_2 · Cited by 16
Sample-Efficient Deep Learning Techniques for Burn Severity Assessment with Limited Data Conditions
Shin, H., Shin, H., Choi, W., Park, J., Park, M., Koh, E., & Woo, H. (2022) · Applied Sciences · doi:10.3390/app12147317 · Cited by 12
A COVID-19 Auxiliary Diagnosis Based on Federated Learning and Blockchain
Wang, Z., Cai, L., Zhang, X., Choi, C., & Su, X. (2022) · Computational and Mathematical Methods in Medicine · doi:10.1155/2022/7078764 · Cited by 11
“[11] provide federated learning application scenarios in biomedicine and confirmed its feasibility in smart healthcare.”
BackgroundFederated Non-negative Matrix Factorization for Short Texts Topic Modeling with Mutual Information
Si, S., Wang, J., Zhang, R., Su, Q., & Xiao, J. Z. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892602 · Cited by 11
“Federated learning (FL) has been proposed to collaboratively train a machine learning model over decentralized data on clients [17], [18].”
BackgroundDecentralized Federated Learning for Over-Parameterized Models
Qin, T., Etesami, S. R., & Uribe, C. A. (2022) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc51059.2022.9992924 · Cited by 11
“Federated Learning has shown great potential in communication efficiency and its capability of preserving data privacy [2], and has exhibited outstanding performance in real-world applications such as keyboard prediction [3] and healthcare [4], [5].”
BackgroundFederated Learning for Clinical Event Classification Using Vital Signs Data
Lee, K. Y., & Rakhmiddin, R. (2023) · Multimodal Technologies and Interaction · doi:10.3390/mti7070067 · Cited by 10
The Impact of Federated Learning on AI-Enhanced Healthcare Delivery
Shahi, A., & Mittal, A. (2023) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-2639-8.ch005 · Cited by 11
SCHNEIDER, P. L., & Xhafa, F. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-823818-9.00019-5 · Cited by 17
Blockchain-based transfer learning for health screening with digital anthropometry from body images
Priya, J. C., Choudhury, T., Khanna, A., et al. (2022) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-022-00363-5 · Cited by 11
“The accelerating influence of mobile gadgets and smartphones has significantly committed to the advancement of wellness screening applications (apps) (Xu et al. 2021).”
BackgroundPractical and Bilateral Privacy-preserving Federated LearningPreprint
Feng, Y., Yang, X., Fang, W., Xia, S., & Tang, X. (2020) · arXiv (Cornell University) · Cited by 11
Sustainable Federated LearningPreprint
Güler, B., & Yener, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.11274 · Cited by 11
FedGL: Federated Graph Learning Framework with Global Self-SupervisionPreprint
Chen, C., Hu, W., Xu, Z., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.03170 · Cited by 11
“Federated learning has a promising application in finance [33], healthcare [51], mobile edge networks [28], and many other industries [28], in which data cannot be directly aggregated for training machine learning models due to factors such as intellectual property rights, privacy protection, and data security.”
BackgroundJaladanki, S. K., Vaid, A., Sawant, A. S., Xu, J., Shah, K., Dellepiane, S., Paranjpe, I., Chan, L., Kovatch, P., Charney, A. W., et al. (2021) · medRxiv · doi:10.1101/2021.07.25.21261105 · Cited by 11
Self-citationKais, S., Bhatia, A. S., & Alam, M. S. (2023) · Research Square · doi:10.21203/rs.3.rs-2723753/v1 · Cited by 13
Partner in Crime: Boosting Targeted Poisoning Attacks Against Federated Learning
Sun, S., Sugrim, S., Stavrou, A., & Wang, H. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3555429 · Cited by 15
“…of applications with dense device deployments at the edge, such as resource allocation for low-latency vehicle-to-vehicle (V2V) communications [3], mobile keyboard prediction by Google [4], intrusion detection in IoT networks [5], [6] and patient similarity analysis in healthcare [7], [8].”
Kanthavel, R., & Dhaya, R. (2025) · Innovation and Emerging Technologies · doi:10.1142/s2737599425500252 · Cited by 10
Kanauzia, R., Singh, M., Gulati, S., Singh, B. M., Arora, N., & Gola, K. K. (2026) · Knowledge and Information Systems · doi:10.1007/s10115-025-02673-2 · Cited by 16
Nemane, S. G., Sarad, V. M., Tulaskar, D. P., Marode, T. P., Bhangdiya, V. K., Agrawal, L., Avthankar, A., Kondylakis, H., Kulkarni, M. B., Bhaiyya, M. L., et al. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10629-0 · Cited by 13
“In other words, for the clinicians, FL presents powerful and interpretable, generalizable models for making timely decisions, and for the patients, FL enables safe interventions, privacy, and quality of life, thus powering scalable and non-intrusive monitoring [40, 110].”
A Federated Learning Approach for Distributed Human Activity Recognition
Concone, F., Ferdico, C., Re, G. L., et al. (2022) · IEEE International Conference on Smart Computing · doi:10.1109/smartcomp55677.2022.00066 · Cited by 10
“Several applications have benefited from the FL paradigm, ranging from finance [18] and monitoring [19], to health-care [20] scenarios.”
An effective and practical gradient inversion attack
Luo, Z., Zhu, C., Fang, L., Kou, G., Hou, R., & Wang, X. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.22997 · Cited by 10
“19 However, with the deepening of the exploration of federal learning, it is found that this field is not as safe as it was designed at the beginning, or federal learning needs to be improved.”
BackgroundLiu, T., Hu, X., & Shu, T. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3237806 · Cited by 10
“The findings in [10] indicate that the slow and unstable convergence of FL model is mainly caused by the weight divergence of the local model updates of different clients.”
BackgroundVo, V. T.-T., Shin, T.-H., Yang, H.-J., Kang, S., & Kim, S.-H. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108104 · Cited by 11
Gottardelli, B., Gouthamchand, V., Masciocchi, C., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-58241-1 · Cited by 10
“This mitigates potential privacy issues and maintains the control and data ownership by each involved party 23–26 .”
Federated learning for digital healthcare: concepts, applications, frameworks, and challenges
Sachin, D. N., Annappa, B., & Ambesange, S. (2024) · Computing · doi:10.1007/s00607-024-01317-7 · Cited by 10
“The survey in [13] examined a selection of available FL strategies for the healthcare domain and offered broad answers to the problems in FL scenarios.”
Privacy Preserving Breast Cancer Prediction with Mammography Images Using Federated Learning
Tabassum, A., Ovi, S. H., Hossain, S., Tonmoy, M. R., Shovon, M. S. H., Hussein, M. R., & Mistry, D. (2024) · Studies in big data · doi:10.1007/978-981-97-3966-0_12 · Cited by 10
Federated Learning in Glaucoma
Hallaj, S., Chuter, B., Lieu, A. C., Singh, P., Kalpathy–Cramer, J., Xu, B. Y., Christopher, M. A., Zangwill, L. M., Weinreb, R. N., & Baxter, S. L. (2024) · Ophthalmology Glaucoma · doi:10.1016/j.ogla.2024.08.004 · Cited by 10
Zhu, L., & Chen, X. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06845-9 · Cited by 11
“LDP and GDP are combined within an FL framework while optimizing communication overhead, demonstrating its effectiveness across multiple data-sets (MNIST and CIFAR-10).”
Noise-Robust Federated Learning With Model Heterogeneous Clients
Fang, X., & Ye, M. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3522573 · Cited by 10
Federated Learning: An Approach for Managing Data Privacy and Security in Collaborative Learning
Jaswal, R., Panda, S. N., & Khullar, V. (2025) · Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) · doi:10.2174/0123520965328724241218110637 · Cited by 13
Panda, S., Dubey, R., Jena, B., Pareek, V., Tsai, L.-W., & Saxena, S. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.112747 · Cited by 11
Federated Learning: Attacks, Defenses, Opportunities and Challenges
Shirvani, G., Ghasemshirazi, S., & Beigzadeh, B. (2024) · International Symposium on Telecommunications · doi:10.1109/ist64061.2024.10843494 · Cited by 20
“B. Types Of Federate Learning 1) Horizontal Federated Learning One of the most researched categories of federated learning is Horizontal Federated Learning (HFL).”
Artificial intelligence in choroid through optical coherence tomography: a comprehensive review
Selvam, A., Driban, M., Ong, J., Bollepalli, S. C., Sahel, J., Chhablani, J. K., & Vupparaboina, K. K. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-024-11067-9 · Cited by 11
“Federated learning, which uses a global model that can be trained on a central server while keeping the sensitive data at their home institution, offers a solution that address the lack of standardization and privacy concerns (Xu et al. 2020).”
InfluentialHosseini, A., & Serag, A. (2025) · IEEE Access · doi:10.1109/access.2025.3555619 · Cited by 10
“Despite its promise, federated learning is not immune to privacy risks, as malicious participants can exploit the training process to infer private information [14].”
Healthcare Industry 5.0: Pareto-Optimal IoT-Based Health Monitoring With Federated Learning
Diamantoulaki, I., Tegos, S. A., Bouzinis, P. S., Sarigiannidis, P. G., Chatzakis, C., Petousis, S., Maglaveras, N., & Karagiannidis, G. K. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3557575 · Cited by 11
“A review of technologies relevant to FL was included in [19], with a particular focus on the biomedical domain, mentioning relevant challenges and progress.”
Interactive Attention Networks for Semantic Text Matching
Zhao, S., Huang, Y., Su, C., Li, Y., & Wang, F. (2020) · Proceedings · doi:10.1109/icdm50108.2020.00095 · Cited by 11
“…user types in some disease-related information in the text [20], [21] (e.g., symptoms, disease name, genetic information, personal characteristics, history, etc.) [22], the system can provide the most relevant articles with treatment, prevention, or prognosis of the corresponding disease [23]–[25].”
BackgroundSelf-citationHaque, R. U., Hasan, A. S. M. T., Daria, A., Qu, Q., & Jiang, Q. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-0852-1_19 · Cited by 13
Evil vs evil: using adversarial examples to against backdoor attack in federated learning
Liu, T., Li, M., Zheng, H., Ming, Z., & Chen, J. (2022) · Multimedia Systems · doi:10.1007/s00530-022-00965-z · Cited by 10
“In particular, benefiting from data feature alignment, horizontal federated learning is successfully deployed in real-world applications, e.g., credit risk prediction and drug discovery [4–7].”
MethodComputationally Efficient Auto-Weighted Aggregation for Heterogeneous Federated Learning
Talukder, Z., & Islam, M. A. (2022) · IEEE International Conference on Edge Computing and Communications (EDGE) · doi:10.1109/edge55608.2022.00015 · Cited by 10
“Due to FL’s privacy enhancement, it is of great interest in healthcare, where sensitive and private patient data is used to train ML systems [4].”
BackgroundEfficient Multi-Layer Stochastic Gradient Descent Algorithm for Federated Learning in E-health
Yu, C., Shen, S., Wang, S., Zhang, K., & Zhao, H. Y. (2022) · International Conference on Communications · doi:10.1109/icc45855.2022.9839100 · Cited by 10
“Learning on edge, such as federated learning, can also enhance privacy in the applications of e-health [3].”
BackgroundOn-device federated learning with fuzzy logic based client selection
Du, Z., Wu, C., Yoshinage, T., Zhong, L., & Ji, Y. (2022) · ACM Conference on Research in Adaptive and Convergent Systems (RACS) · doi:10.1145/3538641.3561490 · Cited by 11
“In order to protect patient privacy, hospitals can keep their data on hospital servers without affecting the development of artificial intelligence [25].”
BackgroundZheng, W., Cao, Y., & Tan, H. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08375-6 · Cited by 10
Wearable Technology for Smart Manufacturing in Industry 5.0
Nguyen, T., Tran, K. D., Raza, A., Nguyen, T., Bui, H. M., & Tran, K. P. (2023) · Springer series in reliability engineering · doi:10.1007/978-3-031-30510-8_11 · Cited by 10
Healthcare Data Protection using Federated Learning Technology
Gobinath, B., & Shanmugam, T. K. (2023) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs56967.2023.10142903 · Cited by 10
Federated generalized linear mixed models for collaborative genome-wide association studies
Li, W., Chen, H., Jiang, X., & Harmanci, A. O. (2023) · iScience · doi:10.1016/j.isci.2023.107227 · Cited by 10
“Federated association testing (rooted from Federated Learning approaches [33, 34]) among different sites present a viable solution for increasing sample sizes while underlying genotype and phenotype data is not explicitly shared.”
STDLens: Model Hijacking-Resilient Federated Learning for Object Detection
Chow, K.-H., Liu, L., Wei, W., Ilhan, F., & Wu, Y. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01568 · Cited by 10
“Federated Learning (FL) for object detection has attracted numerous applications [12], especially in healthcare, with strict privacy regulations protecting patient medical records [25, 27].”
BackgroundDecentralized Federated Learning on the Edge Over Wireless Mesh Networks
Salama, A., Stergioulis, A., Zaidi, S. A. R., & McLernon, D. (2023) · IEEE Access · doi:10.1109/access.2023.3329362 · Cited by 10
Sakhawat, A. R., Fatima, A., Abbas, S., Ahmad, M., & Khan, M. A. (2024) · Advances in business information systems and analytics book series · doi:10.4018/979-8-3693-0839-4.ch012 · Cited by 10
FedAVO: Improving Communication Efficiency in Federated Learning with African Vultures Optimizer
Hossain, M. Z., Imteaj, A., & Shahid, A. R. (2024) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac61105.2024.00069 · Cited by 16
“For these distinctive qualities, FL has been adopted in a wide range of applications such as speech recognition [2], [3], surveillance system [4], [5], health care system [6], [7], Internet of Things (IoT)[8], human stroke prevention [9], [10], and cybersecurity [11].”
BackgroundCommunication-Efficient Secure Aggregation for Federated Learning
Ergun, I., Sami, H. U., & Güler, B. (2022) · Globecom · doi:10.1109/globecom48099.2022.10001481 · Cited by 11
FedABC: Targeting Fair Competition in Personalized Federated Learning
Wang, D., Shen, L., Luo, Y., Hu, H., Su, K., Wen, Y., & Tao, D. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i8.26203 · Cited by 16
Krishnamoorthy, U. (2025) · Analytical Methods · doi:10.1039/d5ay01580d · Cited by 9
Wang, S. (2023) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2023.1269105 · Cited by 11
“FL approaches have been proposed and applied in diverse domains, including finance (Shingi, 2020), healthcare (Xu et al., 2021), and medical image analysis (Courtiol et al., 2019).”
BackgroundData Protection: Privacy-Preserving Data Collection With Validation
Hou, J., Liu, D., Huang, C., Zhuang, W., Shen, X., Sun, R., & Ying, B. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3326299 · Cited by 9
“Data privacy with federated learning: The state-of-the-art federated learning (FL) techniques are widely used to collab-oratively train an ML model based on private data [25], [38].”
MethodMiao, G., Yu, L., Yang, J., Bennett, D. A., Zhao, J., & Wu, S. S. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104581 · Cited by 9
Efficient and Secure Aggregation Framework for Federated-Learning-Based Spectrum Sharing
Li, W.-W., Chen, G.-R., Zhang, X., Wang, N., Ouyang, D., & Chen, C.-X. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3357575 · Cited by 9
“Federated learning has been successfully utilized in various domains, such as smart finance [17], smart healthcare [18], and vehicular networks [19].”
MethodLearned Parameter Compression for Efficient and Privacy-Preserving Federated Learning
Chen, Y., Abrahamyan, L., Hichem Sahli, B., & Deligiannis, N. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3409191 · Cited by 9
“F EDERATED learning (FL) [1] is a promising framework to address the challenges of privacy and data security when training AI models for applications in, for example, mobile Internet [2], healthcare [3], and Internet of things [4], [5].”
BackgroundPeer-to-Peer Federated Learning on Software-Defined Optical Access Network
Pakpahan, A. F., & Hwang, I. (2024) · IEEE Access · doi:10.1109/access.2024.3411639 · Cited by 9
“The adaptation of FL in domains like healthcare further exemplifies its potential, demonstrating how local data processing can be leveraged for sensitive applications [27].”
BackgroundXie, L., Lin, M., Liu, S., Xu, C., Luan, T., Li, C., Fang, Y., Shen, Q., & Wu, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72117-5_56 · Cited by 9
Implantable Medical Device Security
Catuogno, L., & Galdi, C. (2024) · Cryptography · doi:10.3390/cryptography8040053 · Cited by 24
“This would allow, for example, the engineering of diagnosis systems that are trained using medical (sensitive) data provided by different stakeholders [70,71], e.g., different hospitals.”
Non-IID Medical Imaging Data on COVID-19 in the Federated Learning Framework: Impact and Directions
Al-Hafiz, F., & Basuhail, A. A. (2024) · COVID · doi:10.3390/covid4120140 · Cited by 11
A Trusted Federated Learning Method Based on Consortium Blockchain
Yin, X., Wu, X., & Zhang, X. (2024) · Information · doi:10.3390/info16010014 · Cited by 13
Metadata
- Authors
- Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F.
- Affiliations
- Weill Cornell Medicine (US)
- Icahn School of Medicine at Mount Sinai (US)
- United States Department of Defense (US)
- University of Florida (US)
- Corresponding author
- Wang, Fei
- Venue
- Journal of Healthcare Informatics Research
- Published
- 2020-11-12
- Volume
- 5
- Issue
- 1
- Pages
- 1-19
- ISSN
- 2509-4971 (ISSN-L)
- 2509-498X
- PMC
- PMC7659898
- OpenAlex
- W3100779497
- Cited by
- 1,542
- References
- 130
- Citations by year
- 2026: 239 · 2025: 366 · 2024: 314 · 2023: 278 · 2022: 204 · 2021: 111 · 2020: 29
- FWCI
- 61.09
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Healthcare Informatics Research (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1750326)
- National Institute on Aging (P30AG028740)
- Office of Naval Research (N00014-18-1-2585, N00014-18, N00014)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- federated learning, healthcare data privacy, electronic health records, data fragmentation, privacy-preserving machine learning, biomedical applications
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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