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
- Rehak D, Dodds P, Lannom L (2005) A model and infrastructure for federated learning content repositories. In: Interoperability of web-based educational systems workshop, vol 143. Citeseer
- Smith V, Chiang CK, Sanjabi M, Talwalkar AS (2017) Federated multi-task learning. In: Advances in neural information processing systems, pp 4424–4434
Cited by
Sorted by citations
Items 201–300 of 1,496
Rajendran, S., Pan, W., Sabuncu, M. R., Chen, Y., Zhou, J., & Wang, F. (2024) · Patterns · doi:10.1016/j.patter.2023.100913 · Cited by 47
“8,9,10 Almost all healthcare problems involve multiple different data modalities, such as EHR, medical imaging, and genetic sequencing.”
BackgroundSelf-citationSemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence
Tashakori, A., Zhang, W., Wang, Z. J., & Servati, P. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3233599 · Cited by 46
“In federated learning frameworks, users collaboratively train a global model while keeping their data isolated on the edge devices [34], [35], [36].”
BackgroundA systematic review of federated learning from clients’ perspective: challenges and solutions
Shanmugarasa, Y., Paik, H.-Y., Kanhere, S. S., & Zhu, L. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10563-8 · Cited by 55
Federated learning: a comprehensive review of recent advances and applications
Kaur, H., Rani, V., Kumar, M., Sachdeva, M., Mittal, A. K., & Kumar, K. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17737-0 · Cited by 54
“[39] conducted a survey to address the privacy concerns of healthcare records and explored the potential of FL to connect and integrate fragmented healthcare data sources while ensuring data privacy.”
Kumar, R., Waisberg, E., Ong, J., Paladugu, P., Amiri, D., Saintyl, J., Yelamanchi, J., Nahouraii, R., Jagadeesan, R., & Tavakkoli, A. (2024) · Brain Sciences · doi:10.3390/brainsci14121266 · Cited by 52
State of the Art in Parallel and Distributed Systems: Emerging Trends and Challenges
Dai, F., Hossain, M. A., & Wang, Y. (2025) · Electronics · doi:10.3390/electronics14040677 · Cited by 59
Artificial intelligence in cancer: applications, challenges, and future perspectives
Cheng, H., & Shi, S.-S. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02450-3 · Cited by 64
“Meanwhile, client selection protocols, model compression, and update reduction strategies focus on boosting communication efficiency [200, 211, 212].”
A federated learning based approach for loan defaults prediction
Shingi, G. (2020) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw51313.2020.00057 · Cited by 46
“Xu et al proposed a federated system in the field of healthcare to overcome the limitations of centralized approach [23].”
BackgroundApplying Federated Learning in Software-Defined Networks: A Survey
Ma, X., Liao, L., Li, Z., Lai, R. X., & Zhang, M. (2022) · Symmetry · doi:10.3390/sym14020195 · Cited by 50
“For other areas, Gadekallu et al. [26] filled the vacancy in combining big data with FL; Jiang et al. [4] and Xu et al. [2] analyzed the challenges and potentials of FL in smart cities and healthcare, respectively; Xia et al. [27] and Nguyen et al. [28] focused on edge computing and summarized the…”
BackgroundMethodRajendran, S., Xu, Z., Pan, W., Ghosh, A., & Wang, F. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000117 · Cited by 46
Self-citationAmrollahi, F., Shashikumar, S. P., Holder, A. L., & Nemati, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-12497-7 · Cited by 45
Li, A., Marković, M., Edwards, P. J., & Leontidis, G. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122847 · Cited by 54
Artificial intelligence for COVID-19: battling the pandemic with computational intelligence
Xu, Z., Su, C., Xiao, Y., & Wang, F. (2021) · Intelligent Medicine · doi:10.1016/j.imed.2021.09.001 · Cited by 48
“To maintain a balance between privacy and model precision, federated learning (FL) [143] , a framework of constructing a central parameter server to train a global model based on the parameters from multiple local sites that store their own sensitive data, has attracted more attention and offer…”
Self-citationNg, J. S., Lim, W. Y. B., Xiong, Z., Cao, X., Jin, J., Niyato, D. T., Leung, C., & Miao, C. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3139039 · Cited by 45
“FL has found recent successes in applications such as next word prediction model for the Google Keyboard [6], in healthcare [7], [8], and environment monitoring [9].”
BackgroundFederated Learning for Privacy-Preserved Medical Internet of Things
Thilakarathne, N. N., Muneeswari, G., Parthasarathy, V., Alassery, F., Hamam, H., Mahendran, R. K., & Shafiq, M. (2022) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2022.023763 · Cited by 48
An Effective Secured Privacy-Protecting Data Aggregation Method in IoT
Pramanik, S. (2022) · Advances in wireless technologies and telecommunication book series · doi:10.4018/978-1-7998-9312-7.ch008 · Cited by 43
Du, J., Qin, N., Huang, D., Zhang, Y., & Jia, X. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3238724 · Cited by 45
“Multiple data holders can collaboratively construct a shared model via distributed processing, and relevant theories have also been applied to other fields such as science technology, finance, education, etc. [14], [17], [18].”
MethodExplainable Federated Medical Image Analysis Through Causal Learning and Blockchain
Mu, J., Kadoch, M., Yuan, T., Lv, W., Liu, Q., & Li, B. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3375894 · Cited by 46
“For consumers, FL has been used to screen for atrial fibrillation using electrocardiograms captured by smart-watches [1].”
BackgroundMethodWani, R. U. Z., Thabit, F., & Can, Ö. (2024) · Security and Privacy · doi:10.1002/spy2.409 · Cited by 50
Madathil, N. T., Dankar, F. K., Gergely, M., Belkacem, A. N., & Alrabaee, S. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.06.009 · Cited by 58
Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future DirectionsPreprint
Gadekallu, T. R., Pham, Q., Huynh‐The, T., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.04160 · Cited by 43
“These works are only limited to security / privacy / threat aspects of FL. [23, 24, 25] The use of FL for particular applications, e.g., healthcare, vehicular IoT, and open bank are presented.”
BFV-Based Homomorphic Encryption for Privacy-Preserving CNN Models
Wibawa, F., Çatak, F. Ö., Sarp, S., & Kuzlu, M. (2022) · Cryptography · doi:10.3390/cryptography6030034 · Cited by 42
Federated Active Learning for Multicenter Collaborative Disease Diagnosis
Wu, X., Pei, J., Chen, C., Zhu, Y., Wang, J., Qian, Q., Zhang, J., Sun, Q., & Guo, Y. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3227563 · Cited by 42
“Federated learning is widely learned in lots of fields including the medical domain [20], [21].”
BackgroundTaha, Z. K., Yaw, C. T., Koh, S. P., Kiong, T. S., Kadirgama, K., Benedict, F., Tan, J. D., & Balasubramaniam, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3267964 · Cited by 48
“In [7], the author reviewed a selection of typical FL strategies for the healthcare industry while summarizing the overall answer to the problems in FL scenarios.”
BackgroundAlmalki, J., Alshahrani, S. M., & Khan, N. A. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.1778 · Cited by 42
“Going on further, Xu et al. (2021) revealed the use of machine learning in bio-informatics, in which the information was processed and managed using visualization techniques.”
BackgroundAl-Huthaifi, R., Li, T., Al‐Huda, Z., & Li, C. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120482 · Cited by 47
A New Look and Convergence Rate of Federated Multitask Learning With Laplacian Regularization
Dinh, C. T., Vu, T. T., Tran, N. H. K., Dao, M. N., & Zhang, H. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3224252 · Cited by 76
“Digital Object Identifier 10.1109/TNNLS.2022.3224252 from wearable devices in health-care systems [5], [6], [7], or predicting disaster risks from the Internet-of-Things devices in smart cities [8], [9].”
BackgroundInfluentialWang, X., Chen, W., Xia, J., Wen, Z., Zhu, R., & Schreck, T. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3209347 · Cited by 43
“Especially, horizontal federated learning (HFL), which integrates data from the same feature space but distributed at different clients, has been widely used in privacy-aware applications, like healthcare and mobile service [18, 36].”
BackgroundXia, T., Han, J., & Mascolo, C. (2022) · Experimental Biology and Medicine · doi:10.1177/15353702221115428 · Cited by 46
“82,83 86”
Federated Machine Learning for Skin Lesion Diagnosis: An Asynchronous and Weighted Approach
Yaqoob, M. M., Alsulami, M., Khan, M. A., Alsadie, D., Saudagar, A. K. J., & Alkhathami, M. (2023) · Diagnostics · doi:10.3390/diagnostics13111964 · Cited by 45
“2023 , 13 , 1964 FL allows a shared global model to be trained on local clients’ parameters without disclosing sensitive data to the server [4].”
BackgroundPrivacy-preserving Cross-domain Recommendation with Federated Graph Learning
Tian, C., Xie, Y., Chen, X., Li, Y., & Zhao, W. X. (2024) · ACM Transactions on Information Systems · doi:10.1145/3653448 · Cited by 43
Toward Quantum Federated Learning
Ren, C., Yan, R., Zhu, H., Yu, H., Xu, M., Shen, Y., Xu, Y. H., Xiao, M., Dong, Z. Y., Skoglund, M., et al. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3552643 · Cited by 93
“FL has found applications in various domains, including healthcare [21], finance [22], and smart cities [23], [24], where data privacy and security are of paramount importance.”
GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning
Yue, X., Nouiehed, M., & Kontar, R. A. (2022) · INFORMS Journal on Data Science · doi:10.1287/ijds.2022.0022 · Cited by 56
Supriya, Y., & Gadekallu, T. R. (2023) · Journal of Data and Information Quality · doi:10.1145/3575810 · Cited by 42
“Use Case 3: Air Quality Prediction: The PSO technique is used in this article [7] to develop an algorithm that uses UAV swarm intelligence to locate the most dangerous zone in the city.”
BackgroundMethodVeritti, D., Rubinato, L., Sarao, V., Nardin, A. D., Foresti, G. L., & Lanzetta, P. (2023) · Graefe s Archive for Clinical and Experimental Ophthalmology · doi:10.1007/s00417-023-06245-4 · Cited by 46
“Federated learning is the ideal approach for ophthalmology and healthcare due to its capacity to protect patient data while still enabling the AI system to learn [4, 46].”
BackgroundAccelerating Fair Federated Learning: Adaptive Federated Adam
Ju, L., Zhang, T., Salman Zubair, T. O. O. R., & Hellander, A. (2024) · IEEE Transactions on Machine Learning in Communications and Networking · doi:10.1109/tmlcn.2024.3423648 · Cited by 46
“With increasing concerns about data security and privacy protection, federated learning has attracted much research interest [2], [3] and has been proven to work effectively in various application domains [4], [5].”
BackgroundBhardwaj, T., & Sumangali, K. (2025) · Scientific Reports · doi:10.1038/s41598-025-04083-4 · Cited by 42
FedSecurity: A Benchmark for Attacks and Defenses in Federated Learning and Federated LLMs
Han, S., Buyukates, B., Hu, Z., Jin, H., Jin, W., Sun, L., Wang, X., Wu, W., Xie, C., Yao, Y., et al. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671545 · Cited by 55
“Collecting such data to a central server is”
A Comprehensive Survey on Federated Learning: Concept and Applications
Mahlool, D. H., & Alsalihi, M. H. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-19-2069-1_37 · Cited by 43
Alvarellos, M. L., Sheppard, H. E., Knarston, I. M., Davison, C., Raine, N., Seeger, T., Barja, P. P., & Chatzou, M. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2022.1045450 · Cited by 44
Privacy-Preserving Federated Learning in Healthcare
Moon, S., & Lee, W. H. (2023) · International Conference on Electronics, Information, and Communication (ICEIC) · doi:10.1109/iceic57457.2023.10049966 · Cited by 42
“For example, electronic health record (EHR) was implemented to improve the quality of care and patients’ outcome, but it is challenging to share across hospitals due to patients’ private information [6].”
BackgroundLeveraging physiology and artificial intelligence to deliver advancements in health care
Zhang, A., Wu, Z., Wu, E., Wu, M. A., Snyder, M. P., Zou, J., & Wu, J. C. (2023) · Physiological Reviews · doi:10.1152/physrev.00033.2022 · Cited by 44
Federated quanvolutional neural network: a new paradigm for collaborative quantum learning
Bhatia, A. S., Kais, S., & Alam, M. A. (2023) · Quantum Science and Technology · doi:10.1088/2058-9565/acfc61 · Cited by 43
“Since the introduction of federated learning based GBoard application, it has emerged as a research paradigm of future intelligent applications in several domains for collaborative learning such as smart energy infrastructures [4], healthcare [5], genomic research [6], wireless communications [7], geospatial applicati…”
BackgroundA Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications
Upreti, D., Yang, E., Kim, H., & Seo, C. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.048932 · Cited by 46
“FL offers substantial benefits regarding communication cost reduction, such as minimizing latency and lowering the transmit power required for raw data transmission [122].”
Jekel, L., Brim, W. R., von Reppert, M., Staib, L. H., Petersen, G. C., Merkaj, S., Subramanian, H. E., Zeevi, T., Payabvash, S., Bousabarah, K., et al. (2022) · Cancers · doi:10.3390/cancers14061369 · Cited by 41
Heterogeneous data integration methods for patient similarity networks
Gliozzo, J., Mesiti, M., Notaro, M., Petrini, A., Patak, A., Gallardo, A. P., Paccanaro, A., Valentini, G., & Casiraghi, E. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac207 · Cited by 38
Resource management at the network edge for federated learning
Trindade, S., Bittencourt, L. F., & da Fonseca, N. L. S. (2022) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.10.015 · Cited by 58
Towards Efficient Replay in Federated Incremental Learning
Li, Y., Li, Q., Wang, H., Li, R., Zhong, W., & Zhang, G. (2024) · 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/cvpr52733.2024.01218 · Cited by 45
“Recently, FL has attracted growing attention and been applied to various fields such as recommendation systems [7, 24] and smart healthcare [35, 46].”
BackgroundWong, D. Y. L., Lam, M. C., Ran, A. R., & Cheung, C. Y.-L. (2022) · Current Opinion in Ophthalmology · doi:10.1097/icu.0000000000000886 · Cited by 37
FedU: A Unified Framework for Federated Multi-Task Learning with Laplacian Regularization.Preprint
Dinh, C. T., Vu, T. T., Tran, N. H. K., Dao, M. N., & Zhang, H. (2021) · arXiv (Cornell University) · Cited by 39
On the Convergence of Multi-Server Federated Learning With Overlapping Area
Qu, Z., Li, X., Xu, J., Tang, B., Lu, Z., & Liu, Y. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3200016 · Cited by 36
“The static scenario can be considered as the hospital data [21] or environmental monitoring sensors [22], where the clients cannot move and only connect to the corresponding regional server(s).”
BackgroundSelf-citationThe Cost of Training Machine Learning Models Over Distributed Data Sources
Guerra, E., Wilhelmi, F., Miozzo, M., & Dini, P. (2023) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2023.3274394 · Cited by 44
“In the Healthcare scenario, accurate predictions that guarantee the confidentiality of users’ data are required [50].”
BackgroundOliva, A., Kaphle, A., Reguant, R., Sng, L. M. F., Twine, N. A., Malakar, Y., Wickramarachchi, A., Keller, M., Ranbaduge, T., Chan, E. K. F., et al. (2024) · GigaScience · doi:10.1093/gigascience/giae021 · Cited by 43
Ten Challenging Problems in Federated Foundation Models
Fan, T., Gu, H., Cao, X., Chan, C. S., Chen, Q., Chen, Y., Feng, Y., Gu, Y., Geng, J., Luo, B., et al. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3555328 · Cited by 49
“To address these challenges, potential solutions include: (1) Establishing theoretical bounds for dimensions such as privacy, utility, and efficiency.”
ECG data dependency for atrial fibrillation detection based on residual networks
Seo, H.-C., Oh, S. H., Kim, H., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-97308-1 · Cited by 36
Multi-party Diabetes Mellitus risk prediction based on secure federated learning
Su, Y., Huang, C., Zhu, W., Lyu, X., & Ji, F. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104881 · Cited by 35
Data privacy: From transparency to fairness
Wu, C. (2024) · Technology and Society · doi:10.1016/j.techsoc.2024.102457 · Cited by 32
Independent and Identically Distributed (IID) Data Assessment in Federated Learning
Arafeh, M., Hammoud, A., Otrok, H., Mourad, A., Talhi, C., & Dziong, Z. (2022) · Globecom · doi:10.1109/globecom48099.2022.10001718 · Cited by 38
“Federated learning usage are increasing and have been introduced to a wide array of applications such as IoT [1], [15], [18], health care [14], [16], [17], [23], natural language processing [12] and Intelligent Transportation Systems [7], [8], [24].”
BackgroundSurvey on Federated Learning enabling indoor navigation for industry 4.0 in B5G
Alsamhi, S. H., Shvetsov, A. V., Hawbani, A., Shvetsov, A. V., Kumar, S., & Zhao, L. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.06.001 · Cited by 35
Lakhan, A. R., Lateef, A. A. A., Ghani, M. K. A., Abdulkareem, K. H., Mohammed, M. A., Nedoma, J., Martínek, R., & García-Zapirain, B. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101747 · Cited by 35
Mateus, P., Moonen, J., Beran, M., Jaarsma, E., van der Landen, S. M., Heuvelink, J., Birhanu, M., Harms, A. G. J., Bron, E., Wolters, F. J., et al. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104661 · Cited by 31
Artificial Intelligence in Medicine: Text Mining of Health Care Workers’ Opinions
Nitiéma, P. (2022) · Journal of Medical Internet Research · doi:10.2196/41138 · Cited by 40
“Data sets from a given setting are used by a local server and are not shared with other entities; however, the algorithm being developed takes advantage of all the data sets in the network of servers [45-47].”
Yang, Y., Hu, X., Gao, Z.-P., Chen, J.-F., Ni, C., Xia, X., & Lo, D. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2023.3347898 · Cited by 35
Kolobkov, D. S., Sharma, S. M., Медведев, А. В., Lebedev, M., Kosaretskiy, E., & Vakhitov, R. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1266031 · Cited by 40
“While sending the model parameters is more secure than transmitting the sensitive data, this alone may not be enough to guarantee data privacy, since the model parameters may leak information about the data, for instance through gradient updates during the training process (Lim and Chan, 2021;…”
BackgroundNarula, M., Meena, J., & Vishwakarma, D. K. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108128 · Cited by 38
Federated learning for multi-omics: A performance evaluation in Parkinson’s disease
Danek, B. P., Makarious, M. B., Dadu, A., Vitale, D., Lee, P. S., Singleton, A., Nalls, M. A., Sun, J., & Faghri, F. (2024) · Patterns · doi:10.1016/j.patter.2024.100945 · Cited by 37
“Initially developed for learning user behavior patterns on personal mobile devices without breaching individual privacy, 9 FL has found valuable applications in numerous domains, including finance, 10 medicine, 11,12 and the pharmaceutical industry.”
Federated Learning in Smart Healthcare: A Survey of Applications, Challenges, and Future Directions
Nasajpour, M., Pouriyeh, S., Parizi, R. M., Han, M., Mosaiyebzadeh, F., Liu, L., Xie, Y., & Batista, D. M. (2025) · Electronics · doi:10.3390/electronics14091750 · Cited by 48
Ellili, N. O. D., Nobanee, H., Alsaiari, L., Shanti, H. Z., Hillebrand, B., Hassanain, N., & Elfout, L. (2023) · The Journal of Finance and Data Science · doi:10.1016/j.jfds.2023.100102 · Cited by 56
Local-Global Knowledge Distillation in Heterogeneous Federated Learning with Non-IID DataPreprint
Yao, D., Pan, W., Dai, Y., Wan, Y., Ding, X., Jin, H., Xu, Z., & Sun, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.00051 · Cited by 39
“, 2018), healthcare (Xu et al., 2021), finance (Yang et al.”
BackgroundMccradden, M., Odusi, O., Joshi, S., Akrout, I., Ndlovu, K., Glocker, B., Maicas, G., Liu, X., Mazwi, M., Garnett, T., et al. (2023) · 2023 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3593013.3594096 · Cited by 33
“While race-based data is valuable in several respects, it is important to also keep in mind that these labels themselves are social constricts which can be imprecise and ontologically confused [61] [62] [63].”
BackgroundOpen problems in medical federated learning
Yoo, J. H., Jeong, H., Lee, J., & Chung, T. (2022) · International Journal of Web Information Systems · doi:10.1108/ijwis-04-2022-0080 · Cited by 40
Majeed, A., Zhang, X., & Hwang, S. O. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6040127 · Cited by 37
“Apart from the analysis presented in Table 3, some recent surveys have highlighted the synergies in one or more aspects of the FL paradigm [115,116].”
BackgroundEHR privacy preservation using federated learning with DQRE-Scnet for healthcare application domains
Kumar, C. U. O., Gajendran, S., Bhavadharini, R. M., Suguna, M., & Krithiga, R. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110638 · Cited by 37
Privacy-Preserving Machine Learning for Healthcare: Open Challenges and Future Perspectives
Guerra-Manzanares, A., López, L. J. L., Maniatakos, M., & Shamout, F. E. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-39539-0_3 · Cited by 46
“Several highlight recent advancements in federated learning (Xu et al., 2021; Ali et al., 2022; Joshi et al., 2022; Nguyen et al., 2022), cryptographic techniques (Zalonis et al., 2022), or security aspects of ML models, such as adversarial attacks (Liu et al., 2021).”
BackgroundAl-Huthaifi, R., Li, T., Al‐Huda, Z., Huang, W., Luo, Z., & Xie, P. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112029 · Cited by 37
The Role of Blockchain in Transforming Industries Beyond Finance
Vanmathi, C., Farouk, A., Alhammad, S. M., Ramaiah, M., Bhattacharya, S., & Kasyapa, M. S. B. (2024) · IEEE Access · doi:10.1109/access.2024.3468611 · Cited by 40
“FL adapts to smartphone [86], IoT devices [87], [88]health-care [89], [90], autonomous vehicles [91], [92], fraudulent activities detection [93], [94] and insurance sector [95].”
BackgroundAdvancing breast, lung and prostate cancer research with federated learning. A systematic review
Ankolekar, A., Boie, S. D., Abdollahyan, M., Gadaleta, E., Hasheminasab, S. A., Yang, G., Beauville, C., Δικαίος, Ν., Kastis, G. A., Bussmann, M., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01591-5 · Cited by 44
“Balancing effective model training with patient privacy techniques that can add computational overheads and may affect data contents, ensuring data quality and consistency across multiple centres, and maintaining robust model performance and trust-worthiness are pressing concerns 12 .”
InfluentialContrastive learning improves critical event prediction in COVID-19 patients
Wanyan, T., Honarvar, H., Jaladanki, S. K., Zang, C., Naik, N., Somani, S. S., Freitas, J. K. D., Paranjpe, I., Vaid, A., Zhang, J., et al. (2021) · Patterns · doi:10.1016/j.patter.2021.100389 · Cited by 34
“Several strategies have been utilized to mitigate these data and modeling challenges including up- and down-sampling, pre-training, transfer learning, and federated learning, but each has its limitations for use in EHR research.(36) Other than these methods, the role of loss function has yet to be thoroughly investiga…”
Self-citationFederated Learning For Cyber Security: SOC Collaboration For Malicious URL Detection
Khramtsova, E., Hammerschmidt, C., Lagraa, S., & State, R. (2020) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs47774.2020.00171 · Cited by 35
“Recently, numerous FL-based applications have been proposed for healthcare [9], autonomous driving [10], mobile applications [2], keyword spotting [11], The authors of [12] propose to apply FL-based malware defense mechanism by analysing individual logs in cloud ecosystems.”
MethodThakur, A., Sharma, P., & Clifton, D. A. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3134835 · Cited by 35
“To address the aforementioned aspects of healthcare applications, we propose a federated learning [2], [12] framework that is semi-supervised in nature and can be trained for multi-tasking.”
BackgroundMethodSurvival study on deep learning techniques for IoT enabled smart healthcare system
Munnangi, A. K., UdhayaKumar, S., Ravi, V., Sekaran, R., & Suthendran, K. (2023) · Health and Technology · doi:10.1007/s12553-023-00736-4 · Cited by 33
Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph
Lu, Y., Yu, Z., & Suri, N. (2023) · ACM Transactions on Privacy and Security · doi:10.1145/3591354 · Cited by 32
“Given this significant feature of privacy preservation, federated learning has been applied to a wide range of applications, including wireless communications [51], autonomous driving [15], multi-access edge computing [74], smart manufacturing [10], and healthcare [70].”
BackgroundBayBFed: Bayesian Backdoor Defense for Federated Learning
Kumari, K., Rieger, P., Fereidooni, H., Jadliwala, M., & Sadeghi, A.-R. (2023) · 2023 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp46215.2023.10179362 · Cited by 37
“For instance, multiple hospitals can participate in training a global model for cancer classification without revealing individual patients’ cancer records [21], [36], [46].”
ECG Synthesis via Diffusion-Based State Space Augmented Transformer
Zama, M. H., & Schwenker, F. (2023) · Sensors · doi:10.3390/s23198328 · Cited by 40
“…short of providing a comprehensive solution as even intricate technical strategies, such as the implementation of standalone federated learning [8], cannot unilaterally guarantee comprehensive privacy safeguards as training samples from trained models can be reconstructed, as illustrated by…”
BackgroundYao, I. Z., Dong, M., & Hwang, W. Y. K. (2025) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2025.100253 · Cited by 44
Artificial Intelligence (AI) in Pharmaceutical Formulation and Dosage Calculations
Joshi, S., & Sheth, S. (2025) · Pharmaceutics · doi:10.3390/pharmaceutics17111440 · Cited by 39
Alam, M. U., & Rahmani, R. (2021) · Sensors · doi:10.3390/s21155025 · Cited by 32
“FL is a practical approach to ensure low operation cost, better ownership control, and minimal usage of data stored locally in different devices [18,19].”
Bleker, J., Kwee, T. C., Rouw, D. B., Roest, C., Borstlap, J., de Jong, I. J., Dierckx, R., Huisman, H. J., & Yakar, D. (2022) · European Radiology · doi:10.1007/s00330-022-08712-8 · Cited by 32
Quantum Federated Learning: Remarks and Challenges
Larasati, H. T., Firdaus, M., & Kim, H. (2022) · IEEE International Conference on Cyber Security and Cloud Computing / IEEE International Conference on Edge Computing and Scalable Cloud (CSCloud/EdgeCom) · doi:10.1109/cscloud-edgecom54986.2022.00010 · Cited by 37
An Adaptive Model Averaging Procedure for Federated Learning (AdaFed)
Giuseppi, A., Torre, L. D., Menegatti, D., Priscoli, F. D., Pietrabissa, A., & Poli, C. (2022) · Journal of Advances in Information Technology · doi:10.12720/jait.13.6.539-548 · Cited by 33
Review on application progress of federated learning model and security hazard protection
Yang, A., Ma, Z., Zhang, C., Han, Y., Hu, Z., Zhang, W., Huang, X., & Wu, Y. (2022) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.11.006 · Cited by 35
Zhu, W., Shi, L. X., Li, J., Cao, B., Wei, K., Wang, Z., & Huang, T. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3480995 · Cited by 34
“Augmented with sophisticated artificial intelligence techniques, the distributed IoT systems can support intelligent applications over a spectrum of real-world domains, such as fault detection [7], medical diagnosis [8], and image recognition [9].”
Wu, Q., Han, J., Yan, Y., Kuo, Y., & Shen, Z. M. (2025) · Health Care Management Science · doi:10.1007/s10729-025-09699-6 · Cited by 49
“Tiwari et al. [246] utilized a combination of MARL and Federated Learning [247] to minimize the latency of an IoMT system.”
DFML: Dynamic Federated Meta-Learning for Rare Disease Prediction
Chen, B., Chen, T., Zeng, X., Zhang, W., Lu, Q., Hou, Z., Zhou, J., & Helal, S. (2024) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3239848 · Cited by 31
“Sensitive data in local institutions is used for model learning but is not accessible by the global model and its central server [23], [24], [25].”
When Decentralized Optimization Meets Federated Learning
Gao, H., Thai, M. T., & Wu, J. (2023) · IEEE Network · doi:10.1109/mnet.132.2200530 · Cited by 34
“Due to this favorable property, FL has been extensively studied and widely applied to many applications, such as virtual keyboard input suggestion [15] and smart healthcare [14], to name a few.”
BackgroundA survey on federated learning applications in healthcare, finance, and data privacy/data security
Nevrataki, T., Iliadou, A., Ntolkeras, G., Sfakianakis, I., Lazaridis, L., Maraslidis, G., Asimopoulos, N., & Fragulis, G. F. (2023) · AIP Conference Proceedings · doi:10.1063/5.0182160 · Cited by 32
Data-free knowledge distillation via generator-free data generation for Non-IID federated learning
Zhao, S., Liao, T., Fu, L., Chen, C., Bian, J., & Zheng, Z. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106627 · Cited by 31
Self-citationA Federated Learning Approach to Pneumonia Detection
Khan, S. H., & Alam, M. G. R. (2021) · International Conference on Engineering and Emerging Technologies (ICEET) · doi:10.1109/iceet53442.2021.9659591 · Cited by 33
Distributed brain tumor diagnosis using a federated learning environment
Mahlool, D. H., & Abed, M. H. (2022) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v11i6.4131 · Cited by 31
Federated learning approaches for fuzzy cognitive maps to support clinical decision-making in dengue
Hoyos, W., Aguilar, J., & Toro, M. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106371 · Cited by 33
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
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