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
“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]
Experimental — accuracy not yet measuredThe 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 measuredThe cited source says what the claim attributes to it.
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
“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
“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
“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
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Jindal, V., Kukreja, V., Mehta, S., et al. (2023) · IEEE Global Conference for Advancement in Technology (GCAT) · doi:10.1109/gcat59970.2023.10353403 · Cited by 4
“Pseudoperonospora cubensis causes downy mildew, yellow patches on the top leaf surface, and a downy growth on the underside [4].”
BackgroundGoirand, M., Austin, E., & Clay-Williams, R. (2024) · AI and Ethics · doi:10.1007/s43681-024-00466-x · Cited by 4
“From an AI technology point of view, there are technical solutions developed to meet the requirements of one or two principles in isolation, such as privacy with federated learning techniques [23].”
Brennaf, M. S., Yang, P., & Lanfranchi, V. (2023) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom60117.2023.00363 · Cited by 4
“Federated learning is training a shared model utilising decentralised data from many users and aggregating previously learnt models on the central server [7].”
BackgroundAhmadzai, M., & Nguyen, G. (2024) · Future Internet · doi:10.3390/fi16070220 · Cited by 4
“Equation (2) represents FL’s objective function [41].”
Chen, C., Wang, K. I., Li, P., & Sakurai, K. (2024) · International Journal of Networking and Computing · doi:10.15803/ijnc.14.2_123 · Cited by 4
“…of FL and continual learning, has several potential specific applications in the context of edge computing and data privacy preservation: Edge Computing in Healthcare: The integration of FL can be particularly beneficial in healthcare settings where patient data privacy is critical [78, 79].”
BackgroundEnhancing Privacy of Clinical Decision Support Systems with Federated Learning
Dodevski, Z., Starič, K. D., Bogdanova, A. M., & Trajkovikj, V. (2024) · International Conference on Smart and Sustainable Technologies (SpliTech) · doi:10.23919/splitech61897.2024.10612538 · Cited by 5
“[17] Federated learning in healthcare applications mostly performs classification tasks.”
BackgroundZeng, H., Yue, Z., Jiang, Q., Zhang, Y., Shang, L., Zong, R., & Wang, D. (2024) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs60910.2024.00078 · Cited by 4
“While FL has been successfully deployed for many privacy-sensitive distributed applications (e.g., recidivism justice, public health, loan approvals [9], [10]), concerns about the fairness of FL models have been raised [11].”
BackgroundHernández-Peñaloza, G., Uribe, S., Moreno, F., Graf, N. M., & Álvarez, F. (2024) · EJC Paediatric Oncology · doi:10.1016/j.ejcped.2024.100196 · Cited by 4
Blockchain-Based Security Sustainable Framework for IoMT Applications and Industry 5.0
Baniya, P., Agrawal, A., Nand, P., Bhushan, B., & Bhattacharya, P. (2024) · Springer eBooks · doi:10.1007/978-3-031-69336-6_17 · Cited by 4
Remote health monitoring using artificial intelligence
Jain, P., & Palei, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-13816-4.00008-5 · Cited by 4
Németh, G. D., Lozano, M. A., Quadrianto, N., & Oliver, N. (2025) · IEEE Access · doi:10.1109/access.2025.3546478 · Cited by 4
“…solution by design, given that the raw data never leaves the clients and only the model parameters are shared with the central server, which is of great importance in many practical scenarios, including healthcare [8], [9] and finance [10], [11], or intelligent smartphone interfaces [12].”
Aoyon, R. S., Hossain, I., Abdullah-Al-Wadud, M., & Uddin, J. (2025) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2025.060917 · Cited by 4
FedCon: Scalable and Efficient Federated Learning via Contribution-Based Aggregation
Gao, W., Xu, G., & Meng, X. (2025) · Electronics · doi:10.3390/electronics14051024 · Cited by 6
Secure Federated Learning with Blockchain and Homomorphic Encryption for Healthcare Data Sharing
Firdaus, M., & Rhee, K.-H. (2024) · International Conference on Cyberworlds (CW) · doi:10.1109/cw64301.2024.00036 · Cited by 4
“In recent years, the availability of healthcare data from various sources, like clinical institutions, pharmaceutical industries, and insurance companies, has surged [1].”
Nariman, G. S., & Hamarashid, H. K. (2025) · International Journal of Data Science and Analytics · doi:10.1007/s41060-025-00756-5 · Cited by 4
“Xu et al. [14] explores the use of EHRs and other fragmented healthcare data, applying FL to improve data sharing while preserving patient privacy.”
Bojnordi, A. J. J., Nezhad, M. Z., Bagheri, R., Bazrafshan, M., & Sohrabi, B. (2025) · Discover Health Systems · doi:10.1007/s44250-025-00204-y · Cited by 8
“As a result, it seems that data fragmentation creates a significant barrier to the development of viable BDA methods [78].”
Rethinking federated learning as a digital platform for dynamic and value-driven participation
Düsing, C., & Cimiano, P. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.107847 · Cited by 4
Federated Learning Framework for Privacy-Preserving AI in Healthcare
Ammirata, G., Pezzullo, G., Contino, S., Martino, B. D., & Pirrone, R. (2025) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-87778-0_31 · Cited by 5
FedCmp: Byzantine-robust federated learning through clustering model update parameters
Wang, H., Wang, S., Wang, R., & Wang, P. (2025) · Information Sciences · doi:10.1016/j.ins.2025.122377 · Cited by 7
Emerging Trends in Artificial Intelligence in Neuro-Oncology
Chadha, S., Sritharan, D. V., Hager, T., D’Souza, R., & Aneja, S. (2025) · Current Oncology Reports · doi:10.1007/s11912-025-01688-w · Cited by 5
Population health management of human phenotype ontology
Henry, J. A. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1496935 · Cited by 4
“Federated learning addresses challenges in integrating distributed datasets while safeguarding citizen rights, as demonstrated by systematic reviews on privacy-preserving techniques in healthcare data sharing (Xu et al., 2020).”
Federated learning for big data: A survey on opportunities, applications, and future directions
Gadekallu, T. R., Pham, Q., Huynh‐The, T., Feng, H., Fang, K., Pandya, S., Liyanage, M., Wang, W. W., & Nguyen, T. T. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113614 · Cited by 86
“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.”
A Discussion on How to Apply Smart Data for Precision Marketing in the Global Economy: Asian Context
Zhu, S. (2021) · Economit Journal Scientific Journal of Accountancy Management and Finance · doi:10.33258/economit.v1i2.441 · Cited by 4
“From an organisation’s perspective, they should make efforts to reduce the negative effects of the isolated data islands.”
BackgroundAprendizado Federado aplicado à Internet das Coisas
Ramos, H. S., Maia, G., Papa, G., Alvim, M. S., Loureiro, A. A. F., Cardoso-Pereira, I., Campos, D., Filipakis, G., Riquetti, G., Chagas, E. T. C., et al. (2021) · doi:10.5753/sbc.8184.9.5 · Cited by 4
Tashman, A. P. (2022) · Advances in Chronic Kidney Disease · doi:10.1053/j.ackd.2022.05.003 · Cited by 7
Kidd, B., Wang, K., Xu, Y., et al. (2022) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789811270611_0044 · Cited by 4
FBN: Federated Bert Network with client-server architecture for cross-lingual signature verification
Xie, L., Wu, Z., Zhang, X., & Li, Y. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109681 · Cited by 5
RAFL: A Robust and Adaptive Federated Meta-Learning Framework Against Adversaries
Yu, Z., Lu, Y., & Suri, N. (2023) · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS) · doi:10.1109/mass58611.2023.00068 · Cited by 5
“FL has been adopted across applications such as finance [2], healthcare [3] and Multi-access Edge Computing (MEC) [4].”
MethodMachine Learning Techniques and Privacy Concerns in Human-Computer Interactions: A Systematic Review
He, J., Cao, T., & Duffy, V. G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-48057-7_23 · Cited by 4
Chen, C., Wang, K. I., Li, P., & Sakurai, K. (2023) · International Symposium on Computing and Networking · doi:10.1109/candar60563.2023.00009 · Cited by 5
“…of FL and continual learning, has several potential specific applications in the context of edge computing and data privacy preservation: • Edge Computing in Healthcare: The integration of FL can be particularly beneficial in healthcare settings where patient data privacy is critical [56], [57].”
BackgroundMinimizing Layerwise Activation Norm Improves Generalization in Federated Learning
Yashwanth, M., Nayak, G. K., Rangwani, H., Singh, A., Babu, R. V., & Chakraborty, A. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00228 · Cited by 4
“FL’s privacy-preserving nature has made it increasingly popular in various domains, including smart-phones [5,23], Internet of Things [19,32] and the healthcare industry [21,26,30].”
BackgroundWill You Participate? Exploring the Potential of Robotics Competitions on Human-Centric Topics
Zhang, Y., Vasco, M., Björkman, M., & Kragić, D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-60412-6_18 · Cited by 4
Decentralized Federated Learning: A Defense Against Gradient Inversion Attack
Guang-xi, L., Xiong, Z., Li, R., & Li, W. (2023) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-27041-3_4 · Cited by 5
Multimodal Privacy-preserving Mood Prediction from Mobile Data: A Preliminary StudyPreprint
Liu, T., Liang, P. P., Muszyński, M., Ishii, R., Brent, D. A., Auerbach, R. P., Allen, N. B., & Morency, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.02359 · Cited by 4
“…parallel to improving predictive performance, a recent focus has been on improving the privacy of real-world machine learning models through techniques such as differential privacy [6–8] and federated learning [22, 10, 20], especially for healthcare data (e.g. EHRs [33] and wearable devices [4]).”
BackgroundGlobal Knowledge Distillation in Federated Learning.Preprint
Pan, W., & Sun, L. (2021) · arXiv (Cornell University) · Cited by 4
Private Language Model Adaptation for Speech RecognitionPreprint
Liu, Z., Li, K., Bakshi, S., & Peng, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.10026 · Cited by 5
“To resolve this privacy concern, federated learning (FL) [14, 15, 16], a distributed learning technique, has been proposed and applied in many fields including recommendation [17], keyboard suggestion [18, 19], keyword spotting [20], health care [21], and more recently, ASR including hybrid acoustic models and end-to-…”
Rajendran, S., Xu, Z., Pan, W., et al. (2022) · medRxiv · doi:10.1101/2022.08.30.22279382 · Cited by 4
“FL is one mechanism of training ML models across multiple decentralized sites holding local data samples without exchanging them [18].”
BackgroundSelf-citationMeng, W., Yang, Q., Xu, J., et al. (2024) · medRxiv · doi:10.1101/2024.07.07.24310055 · Cited by 4
Self-citationAnkolekar, A., Boie, S. D., Abdollahyan, M., Gadaleta, E., Hasheminasab, S. A., Yang, G., Beauville, C., Δικαίος, Ν., Kastis, G. A., Bussmann, M., et al. (2024) · medRxiv · doi:10.1101/2024.08.08.24311681 · Cited by 6
“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 trustworthiness are pressing concerns [9].”
InfluentialGaps in Artificial Intelligence Research for Rural Health in the United States: A Scoping ReviewPreprint
Brown, K. E., & Davis, S. E. (2025) · medRxiv · doi:10.1101/2025.06.26.25330361 · Cited by 4
“Synthetic data generation (32) and federated learning (33) are two technical approaches that could help mitigate these sample size and data representativeness concerns, but such approaches have yet to be applied to support AI in rural health and may require additional computational and analytic…”
Saidi, A. A., Amira, A., & Nouali, O. (2025) · Cluster Computing · doi:10.1007/s10586-025-05715-0 · Cited by 4
“Blockchain removes the need for centralized aggregation and ensures tamper resistance, though it increases system complexity and storage demands [17].”
Jafarigol, E., Trafali̇s, T. B., Razzaghi, T., & Zamankhani, M. (2024) · Springer optimization and its applications · doi:10.1007/978-3-031-74006-0_4 · Cited by 7
Li, X., Tao, X., Chen, G., You, L., & Sun, Y. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3573910 · Cited by 4
“…paradigm, Federated learning (FL) [3] emerges to engage private-preserving collaboration among heterogeneous IoT devices to support applications in various domains, e.g., smart city [4], [5], [6], autonomous vehicles [7], [8], [9], [10], [11], and personal healthcare [12], [13], [14], [15].”
Dubey, M., Tembhurne, J. V., & Makhijani, R. (2025) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2025.3575420 · Cited by 5
“FL focuses on numerous domains, including personalized medical informatics [22], collaborative model building [23], and privacy-preserving applications in medical imaging [25].”
A Federated Learning Architecture for Bird Species Classification in Wetlands
Mulero-Pérez, D., Rodriguez-Juan, J., Ramírez-Gordillo, T., Benavent-Lledó, M., Ruiz-Ponce, P., Ortiz-Pérez, D., Hernandez-Lopez, H., Iarovikov, A., García‐Rodríguez, J., Sebastián‐González, E., et al. (2025) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan14040071 · Cited by 3
Zhang, P., Zhang, Q., Hu, X., Chi, W., & Yang, W. (2025) · Ophthalmology and Therapy · doi:10.1007/s40123-025-01209-9 · Cited by 4
“To address this, federated learning—by enabling distributed training of global models while ensuring that raw patient data never leaves local institutions—offers a privacy-preserving solu-tion for multicenter data collaboration [87–89].”
Feng, S., Huang, S., Zhou, Z., Zhang, B., Huang, C., Chen, T., Zhou, C., Wu, S., Zhu, J., Chen, J., et al. (2025) · JOR Spine · doi:10.1002/jsp2.70090 · Cited by 3
Bayesian federated causal inference and its application in manufacturing
Xiao, X., Alharbi, K. A., Zhang, P., Qin, H., & Yue, X. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02665-7 · Cited by 6
Mateus, P., Savino, M., Capocchiano, N. D., et al. (2025) · Medical Physics · doi:10.1002/mp.18034 · Cited by 3
“13 Hence, federated learning permits leveraging the data without the risks derived from data sharing.”
Khan, A., Cruz, B. D., Nepal, N., et al. (2025) · IEEE Communications Standards Magazine · doi:10.1109/mcomstd.2025.3599152 · Cited by 3
“…fraud detection, e-PrescripChain creates a robust system that not only ensures data integrity and transparency but also enhances the ability to proactively detect and prevent fraudulent activities in real-time, addressing the limitations of centralised e-prescription systems comprehensively [6].”
Federated adaptive aggregation: improving privacy and scalability in healthcare AI
Haripriya, R., Khare, N., Pandey, M., & Biswas, S. (2025) · Cluster Computing · doi:10.1007/s10586-025-05290-4 · Cited by 5
“The integration of AI-driven techniques has revolutionized medical image processing, offering more accurate, consistent, and automated solutions for disease diagnosis and prediction [4, 5].”
Securing Patient Privacy and Data Protection by Federated Learning in AI-Driven Healthcare
Sarker, D., Sadi, M. T. H., Gomes, D. J., & Hasan, M. M. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-3306-9.ch012 · Cited by 3
How data barriers shape international strategy
Kolbjørnsrud, V., & Rademaker, L. (2025) · Global Strategy Journal · doi:10.1002/gsj.70004 · Cited by 4
A Collaborative Framework for Disease Prediction Using Machine Learning
Baker, O. F., Subaramaniam, K., Shibghatullah, A. S., Shaffiei, Z. A., & Hamzah, A. (2025) · IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) · doi:10.1109/iicaiet67254.2025.11265309 · Cited by 3
“FL enables decentralized training of ML models across multiple institutions while ensuring that raw data never leaves its source [12], [13], [14].”
A Survey on Privacy-Preserving Enabled Healthcare Services Focusing on Robust Aggregation
Aggarwal, M., Khullar, V., Goyal, N., Jayaprakash, B., Bharathi, B., Sahu, P. K., Sharma, G., & Bhardwaj, V. (2025) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-025-01020-1 · Cited by 4
“FL [27] Addresses FL issues and suggests a FedHealth’s data aggregation transfer learning (TL) platform for smart wearable health-care 2021 [28] Uses IoMT devices and a cloud-based aggregator to investigate how scheduling restrictions affect FL convergence rates 2021 [29] Explores several…”
Cheng, J., Tian, J., Spoto, F., Azhir, A., Mork, D., & Estiri, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-32656-w · Cited by 3
A Two-Stage Federated Learning Framework for Human Activity and Anomaly Detection in IoMT
Salimzhanov, A., Tynyssova, A., Tursynbekov, T., Tulebayeva, S., Yelmagambetova, A., Ever, E., & Yazici, A. (2025) · International Conference on the Internet of Things (IoT) · doi:10.1145/3770501.3770510 · Cited by 3
“Traditional centralized cloud systems [11, 16] provide IT scalability but face critical limitations, including high latency, bandwidth constraints, and significant data security and privacy vulnerabilities [31].”
Kommusaar, J., Elunurm, S., Chomutare, T., Kangasniemi, M. K., Salanterä, S., & Peltonen, L. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106242 · Cited by 8
Implementing XAI in life sciences: Key challenges and pathways to solutions
KREJCAR, O., Abdullah, J., & Namazi, H. (2026) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2026.100153 · Cited by 4
Polato, M., Hammer, B., Röder, M., & Schleif, F.-M. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.132671 · Cited by 7
Zhang, Y., Wang, L., Li, M., Gai, K., & Wang, J. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3654756 · Cited by 3
“In recent years, it has been widely applied in various fields such as industry [3], [4], [5], [6], [7], healthcare [8], [9], [10], communications [11], [12], [13], and generative artificial intelligence [14], [15], [16].”
Awan, M. Z., Khan, N. A., Strakoš, P., Jhangeer, A., & Řı́ha, L. (2026) · Scientific Reports · doi:10.1038/s41598-026-40581-9 · Cited by 3
Abbas, S. R., Abbas, Z., Rehman, M. U., & Lee, S. W. (2026) · Digital Health · doi:10.1177/20552076261420985 · Cited by 5
Khedekar, D., Pokarna, H., Sahay, A., Richhariya, B., Mahapatra, T., & Rajput, A. S. (2026) · Smart Health · doi:10.1016/j.smhl.2026.100649 · Cited by 4
Comparison of federated learning frameworks for discrete event manufacturing systems
Tapia, E., López-Novoa, U., Pinilla, L. S., & de Lacalle, L. N. L. (2026) · International Journal of Data Science and Analytics · doi:10.1007/s41060-026-01185-8 · Cited by 4
A Comprehensive Study on Healthcare Datasets Using AI Techniques
Mistry, S., Wang, L., Islam, Y., & Osei, F. A. J. (2022) · Electronics · doi:10.3390/electronics11193146 · Cited by 4
Coutinho‐Almeida, J., Cruz-Correia, R. J., & Rodrigues, P. P. (2024) · Scientific Reports · doi:10.1038/s41598-024-61371-1 · Cited by 3
“Even though distributed learning has been receiving a lot of attention recently, only some of its concepts have been focused on, mainly distributed-deep learning with a federated learning approach 13,14 .”
BackgroundA Federated Learning Framework via Decentralized Data Valuation for Chronic Disease Healthcare
Li, J., He, J., Hu, L., & Wu, C. (2024) · International Conference on Intelligent Medicine and Image Processing (IMIP) · doi:10.1145/3669828.3669837 · Cited by 3
DCFL: Non-IID Awareness Dataset Condensation Aided Federated Learning
Wang, X., Sha, S., & Sun, Y. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650791 · Cited by 3
“At present, FL has been widely used in keyboard word spotting [3], driver activity recognition [4], speech recognition [5], health monitoring [6], and other domains [7]–[9].”
MethodJu, P., Yang, H., Liu, J., et al. (2024) · ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc) · doi:10.1145/3641512.3686382 · Cited by 3
Federated Learning Approaches for Decentralized Data Processing in Edge Computing
Janaki, G., & Umanandhini, D. (2024) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec61587.2024.10722731 · Cited by 3
Towards Federated Learning for Morphing Attack Detection
Robledo-Moreno, M., Borghi, G., Domenico, N. D., et al. (2024) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb62174.2024.10744518 · Cited by 3
“It has found applications in diverse fields, especially on those systems based on sensitive information, from healthcare [61] to finance [7], i.e. in domains where data sharing is heavily restricted [17].”
BackgroundGAN for MRI Reconstruction in Federated Learning
Morsel, H., Samrouth, K., Bakir, N., & Pak, V. (2024) · International Conference on Smart Systems and Power Management (IC2SPM) · doi:10.1109/ic2spm62723.2024.10841358 · Cited by 3
“The rapid development in digital health and the increase of presence of healthcare data available derive new insights that improve care delivery [5], however electronic health records(EHR) of patients in hospitals, clinical institutions and insurance companies are sensitive and private where there…”
Wang, H., Gu, R., Wang, J., Zhang, X., & Wei, H. (2025) · IEEE Access · doi:10.1109/access.2025.3536001 · Cited by 4
Zhang, H., Liu, L., Weng, K., & Lu, L. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3580409 · Cited by 4
“FL has been widely used in privacy-sensitive fields, such as healthcare [27], [28], [29], [30], [31], finance [32], [33], [34], [35], and the Internet of Things [36], [37], [38].”
Personalized federated learning with hierarchical reweighting for multi-center clinical prediction
Yang, X., Wan, D., Han, G., Zhang, W., & Tang, W. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.109015 · Cited by 4
FedPD: personalized federated learning based on partial distillation
Yang, X., Feng, J.-Y., Guo, S.-Y., Fang, B., & Liao, Q. (2025) · Frontiers of Computer Science · doi:10.1007/s11704-025-40840-4 · Cited by 5
Baru, C. K., Pozmantier, M., Altıntaş, İ., et al. (2022) · AI Magazine · doi:10.1002/aaai.12042 · Cited by 3
Federated Lung Cancer Prediction Using Histopathological Medical Images
Ayekai, B. J., Wenyu, C., Hailemichael, M. T., et al. (2022) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip56608.2022.10016519 · Cited by 3
“Recently, FL has received a lot of interest from the medical image community, where health research has focused a lot on its emphasis on protecting patient privacy, particularly in COVID-19 [10].”
BackgroundCulture intelligent workflow, structure, and steps
Henry, J. A. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.985469 · Cited by 4
“Learning communities expand on biomedical space to GIPES dimensions to improve wellbeing and social system challenges as federated models provide a rational on ontology and hospital eXamination solutions for socioeconomic success (Hill, 1984; Xu et al., 2020).”
BackgroundOn the Trade-off Between Benefit and Contribution for Clients in Federated Learning in Healthcare
Düsing, C., & Cimiano, P. (2022) · International Conference on Machine Learning and Applications · doi:10.1109/icmla55696.2022.00257 · Cited by 3
“It has been shown, however, that EHR data is fragmented [3] and shows a high regional variance [4] across providers.”
BackgroundCommunication-Efficient Federated Linear and Deep Generalized Canonical Correlation Analysis
Shrestha, S., & Fu, X. (2023) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2023.3265886 · Cited by 3
Group Personalized Federated Learning
Liu, Z., Hui, Y., & Peng, F. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094708 · Cited by 3
“FL has been applied in many fields including recommendation [4], smart keyboard suggestion [5, 6], keyword spotting [7], health care [8], and automatic speech recognition (ASR) [9, 10, 11].”
MethodTuan, N. A., Xu, R., Moe, S. J. S., & Kim, D. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3285618 · Cited by 3
“Some of the popular applications of federated learning include health-care [21], autonomous vehicles [22], financial services [23], etc.”
BackgroundBanda, J. M., Shah, N. H., & Periyakoil, V. S. (2023) · JAMIA Open · doi:10.1093/jamiaopen/ooad043 · Cited by 4
“55,56 Rather, we recommend using federated learning, as this is starting to be more accepted in large research networks, 59 or use ensembles using multiple datasets, when available within a single site or research facility.”
MethodMenegatti, D., Pietrabissa, A., Manfredi, S., & Giuseppi, A. (2023) · Mediterranean Conference on Control and Automation (MED) · doi:10.1109/med59994.2023.10185743 · Cited by 3
“…finding use in domains such as Internet of Things (IoT) [14], [15], modern communication systems [16], [17] and even healthcare [12], [18], [19], where its privacy-preserving features allowed federated systems to cooperate in more effective ways while assuring patients’ rights to privacy…”
BackgroundCommunity-Aware Federated Video Summarization
Wan, F., Wang, J., Duan, H., et al. (2023) · doi:10.1109/ijcnn54540.2023.10191101 · Cited by 3
“FL has been applied in smart city [5], recommender system [6] and healthcare [7] fields.”
MethodFine-Tuning Network in Federated Learning for Personalized Skin Diagnosis
Lee, K., Lee, H., Cavalcanti, T. C., Kim, S., Fakhri, G. E., Lee, D. H., Woo, J., & Hwang, J. Y. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43898-1_37 · Cited by 3
Xu, B., Zhang, Y., Fan, Z., Han, L., & Shen, Z.-X. (2023) · Information & Management · doi:10.1016/j.im.2023.103908 · Cited by 3
Pezoulas, V. C., Kalatzis, F., Exarchos, T. P., Goules, A. V., Tzioufas, A. G., & Fotiadis, D. I. (2024) · Patterns · doi:10.1016/j.patter.2023.100893 · Cited by 3
“8 Toward this direction, batch processing methods such as online learning and meta-learning 9,10 have been proposed in the literature, where the former 9 uses stochastic optimization to update an existing estimator on upcoming training batches, whereas the latter 10 focuses on the aggregation of…”
MethodWhen Neural Network Architecture Search Meets Federated Learning Parameter Efficient Fine Tuning
Wen, X., & Xu, S. (2023) · International Conference on Intelligent Communications and Computing (ICICC) · doi:10.1109/icc59986.2023.10421429 · Cited by 3
“Leveraging federated learning (FL) [4] for PTM fine-tuning has emerged as an ideal choice for privacy-sensitive applications, encompassing but not limited to healthcare [5], vehicular networks [6], and artificial intelligence generated content (AIGC) [7].”
BackgroundFederated Learning as a Solution for Problems Related to Intergovernmental Data Sharing
Sprenkamp, K., Fernandez, J. D., Eckhardt, S., & Zavolokina, L. (2023) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2023.208 · Cited by 3
Federated Learning with MLPerfTiny Tasks and Server-side Momentum
Quizon, L. R. A., & Alvarez, A. B. (2024) · International Conference on Machine Learning and Soft Computing (ICMLSC) · doi:10.1145/3647750.3647756 · Cited by 5
“Fed-erated learning has found use in applications like wake words [1], smart keyboards [5], and healthcare [21] which particularly benefits from its privacy.”
BackgroundLoss Aware Federated Learning for Service Migration in Multimodal E-Health Services
Singh, H. K., Pratap, A., Yadav, R. N., & Das, D. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3399658 · Cited by 4
“Additionally, healthcare model needs to be both privacy-preserving and accurate with minimum value of loss function [8], [9].”
BackgroundMitigating Distributed Backdoor Attack in Federated Learning Through Mode Connectivity
Walter, K., Mohammady, M., Nepal, ., & Kanhere, S. S. (2024) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3634737.3637682 · Cited by 4
“Federated Learning (FL) is an emerging trend in collaborative machine learning, finding applications in diverse domains such as pharmaceutical research [1], credit risk management [2], cardiac event prediction [9], mobile keyboard prediction [12, 20] and health-care informatics [36].”
BackgroundDistributionally Robust Clustered Federated Learning: A Case Study in Healthcare
Konti, X., Riess, H., Giannopoulos, M., Shen, Y., Pencina, M. J., Economou-Zavlanos, N., & Zavlanos, M. M. (2024) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc56724.2024.10886134 · Cited by 5
“To address the high communication overhead of typical CFL methods [11], more efficient algorithms have been recently developed.”
Towards a Multi-Modular Decentralized System for Dealing with EHR Data
Mehta, J., Desai, R., Mehta, J., Gandhi, D., & D’Mello, L. (2022) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs54159.2022.9785302 · Cited by 3
Gupta, N., & Gupta, B. (2022) · International Journal of Information Technologies and Systems Approach · doi:10.4018/ijitsa.307025 · Cited by 3
Specifications for the Routine Implementation of Federated Learning in Hospitals Networks
Lamer, A., Filiot, A., Bouillard, Y., Mangold, P., Andrey, P., & Schiro, J. (2021) · Studies in health technology and informatics · doi:10.3233/shti210134 · Cited by 3
“Other studies implemented SupportVector Machine, Principal Components Analysis or Neural Networks using FL [4].”
MethodAutexier, S., Lüth, C., & Drechsler, R. (2022) · Springer eBooks · doi:10.1007/978-3-658-33597-7_40 · Cited by 3
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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