The DOI matches its Crossref record; 117 of the 130 references it cites were found in a registry or an index; 30 of the 45 sources its claims cite were checked for support.
Federated Learning for Healthcare Informatics
Open accessAbstract
With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies, and pharmaceutical industries, among others. This access provides an unprecedented opportunity for data science technologies to derive data-driven insights and improve the quality of care delivery. Healthcare data, however, are usually fragmented and private making it difficult to generate robust results across populations. For example, different hospitals own the electronic health records (EHR) of different patient populations and these records are difficult to share across hospitals because of their sensitive nature. This creates a big barrier for developing effective analytical approaches that are generalizable, which need diverse, "big data." Federated learning, a mechanism of training a shared global model with a central server while keeping all the sensitive data in local institutions where the data belong, provides great promise to connect the fragmented healthcare data sources with privacy-preservation. The goal of this survey is to provide a review for federated learning technologies, particularly within the biomedical space. In particular, we summarize the general solutions to the statistical challenges, system challenges, and privacy issues in federated learning, and point out the implications and potentials in healthcare.
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Claims the paper makes
14 No basis given · 10 Own interpretation · 9 Supported · 8 Partly supported · 3 Sources not found · 3 No full text of the sources · 1 Couldn’t confirm · 1 Not checked yet · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Another commonly used method is globally sharing a small portion of data between all the clients [75, 111]. The shared subset is required containing a uniform distribution over classes from the central server to the clients.”
§ Federated Learning for Healthcare Informatics > Federated Learning > Statistical Challenges of Federated Learning
What the sources say
- Could not verifycites [75] Nishio T, Yonetani R (2018) Client selection for federated learning with heterogeneous resources in mobile edge. arXiv:1804.08333 sourceExperimental — accuracy not yet measured
The Experimental Setup (p. 5) describes IID and non-IID data assignments, but not sharing a small, class-balanced subset among clients. The Results (p. 7) discuss increasing client participation or using model compression to alleviate non-IID effects. Searches for shared data and uniform class distribution found no passage addressing the claimed method. (LLM, conf=0.76)
- Supportedcites [111] Zhao Y, Li M, Lai L, Suda N, Civin D, Chandra V (2018) Federated learning with non-iid data. arXiv:1806.00582 source
Section 4.2 (p. 7) proposes a small, globally shared dataset with a uniform distribution over classes, distributed from the cloud to clients to improve federated learning with non-IID data. Section 4.2 (p. 8) reports improved accuracy using this strategy. (LLM, conf=0.93)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Could not verify
Every source the paper cites for this claim was checked and supports it.
In the paper
“When the component distributions are actually different, pluralism can outperform the “ideal” IID baseline.”
§ Federated Learning for Healthcare Informatics > Federated Learning > Statistical Challenges of Federated Learning
What the sources say
- Supportedcites [28] Eichner H, Koren T, McMahan HB, Srebro N, Talwar K (2019) Semi-cyclic stochastic gradient descent. arXiv:1904.10120 source
The Summary (p. 8) states the claim directly, adding that the paper's experiments illustrate it. Section 4 (p. 4) explains that separate predictors can benefit when component subproblems conflict. (LLM, conf=0.95)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“It is demonstrated can work with certain non independent identical distribution (non-IID) data by requiring all the clients to share the same model.”
§ Federated Learning for Healthcare Informatics > Federated Learning > Statistical Challenges of Federated Learning
What the sources say
- Supportedcites [69] McMahan B, Moore E, Ramage D, Hampson S, Arcas BA (2017) Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics, pp 1273–1282 source
The Experimental Results (p. 6) report that FedAvg achieved substantial speedups on pathologically non-IID MNIST data and worked on non-IID Shakespeare data. The FederatedAveraging Algorithm (p. 4) describes clients updating a shared starting model and the server averaging their models. The paper does not establish that sharing a model alone is sufficient for every non-IID setting, but the claim is limited to certain data. (LLM, conf=0.88)
boostedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Most centralized models are trained on the aggregated training samples obtained from the samples drawn from the local clients [96, 111]. Intrinsically, the centralized model is trained to minimize the loss with respect to the uniform distribution [73]”
§ Federated Learning for Healthcare Informatics > Federated Learning > Statistical Challenges of Federated Learning
What the sources say
- Not matchedcites [96] Smith V, Chiang CK, Sanjabi M, Talwalkar AS (2017) Federated multi-task learning. In: Advances in neural information processing systems, pp 4424–4434
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Partly supportedcites [111] Zhao Y, Li M, Lai L, Suda N, Civin D, Chandra V (2018) Federated learning with non-iid data. arXiv:1806.00582 sourceExperimental — accuracy not yet measured
Section 3.1 (p. 4) describes centralized training as optimizing population loss, but it does not say the population distribution is uniform. Sections 4.1–4.2 (p. 7) specify a uniform distribution over classes only for a separate, globally shared dataset used in the authors’ data-sharing strategy. That does not establish that centralized models generally minimize loss with respect to a uniform distribution. (LLM, conf=0.84)
- Not matchedcites [73] Mohri M, Sivek G, Suresh AT (2019) Agnostic federated learning. In: Chaudhuri K, Salakhutdinov R (eds) Proceedings of the 36th International conference on machine learning, proceedings of machine learning research, vol 97. PMLR, Long Beach, pp 4615–4625
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
Every source the paper cites for this claim was checked and supports it.
In the paper
“The performance of convolutional neural networks trained with FedAvg algorithm can reduce significantly due to the weight divergence [111].”
§ Federated Learning for Healthcare Informatics > Federated Learning > Statistical Challenges of Federated Learning
What the sources say
- Supportedcites [111] Zhao Y, Li M, Lai L, Suda N, Civin D, Chandra V (2018) Federated learning with non-iid data. arXiv:1806.00582 source
The Introduction (p. 1) reports substantial accuracy reductions for CNNs trained with FedAvg on highly skewed non-IID data. The Introduction (p. 2) attributes that reduction to weight divergence, directly supporting the claim's explanation. (LLM, conf=0.94)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“For example, Obermeyer et al. [76] found that a commonly used algorithm to determine enrollment in specific health programs was biased against African Americans, assigning the same level of risk to healthier Caucasian patients.”
§ Federated Learning for Healthcare Informatics > Applications > Healthcare
What the sources say
- Supportedcites [76] Obermeyer Z, Powers B, Vogeli C, Mullainathan S (2019) Dissecting racial bias in an algorithm used to manage the health of populations. Science 366(6464):447–453 source
The paper’s opening section says the widely used algorithm helps identify patients for additional care and that, at a given risk score, Black patients are considerably sicker than White patients. The “Health disparities conditional on risk score” section likewise reports that Black patients are less healthy than White patients at any level of algorithm prediction. Page numbers are not provided in our copy. (LLM, conf=0.94)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Thus, some researchers combine DP with SMC to reduce the growth of noise injection as the number of parties increases without sacrificing privacy while preserving provable privacy guarantees, protecting against extraction attacks and collusion threats [18, 100].”
§ Federated Learning for Healthcare Informatics > Federated Learning > Privacy and Security
What the sources say
- Could not verifycites [18] Cheng K, Fan T, Jin Y, Liu Y, Chen T, Yang Q (2019) Secureboost: a lossless federated learning framework. arXiv:1901.08755 sourceExperimental — accuracy not yet measured
The Introduction (p. 1) describes noise injection in differential privacy, and the Security Discussion (p. 5) mentions secure multi-party computation as a way to limit information leakage. Neither passage says the methods are combined or that combining them reduces noise growth as the number of parties increases. Searches for that combination, noise growth, and party count found no supporting passage. (LLM, conf=0.76)
- Not checked — no full text of the sourcecites [100] Truex S, Baracaldo N, Anwar A, Steinke T, Ludwig H, Zhang R (2018) A hybrid approach to privacy-preserving federated learning. arXiv:1812.03224 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Could not verify
Every source the paper cites for this claim was checked and supports it.
In the paper
“Therefore, federated learning has a promising application in NLP like virtual keyboard prediction [7, 39, 70].”
§ Federated Learning for Healthcare Informatics > Applications > Others
What the sources say
- Supportedcites [7] Bonawitz K, Eichner H, Grieskamp W, Huba D, Ingerman A, Ivanov V, Kiddon C, Konecny J, Mazzocchi S, McMahan HB, et al. (2019) Towards federated learning at scale: System design. arXiv:1902.01046 source
The Applications section (p. 8) describes Gboard using federated learning for keyboard content suggestions and next-word prediction. It also reports that the federated next-word model outperformed comparison models in live experiments, supporting the characterization of keyboard prediction as a promising application. (LLM, conf=0.94)
- Supportedcites [39] Hard A, Rao K, Mathews R, Beaufays F, Augenstein S, Eichner H, Kiddon C, Ramage D (2018) Federated learning for mobile keyboard prediction. arXiv:1811.03604 source
The Abstract (p. 1) describes training a language model with federated learning for next-word prediction in a smartphone virtual keyboard and reports better prediction recall than server-based training. The Conclusion (p. 6) says the federated model outperformed the comparison models on that task, supporting the claim that this is a promising NLP application. (LLM, conf=0.94)
- Supportedcites [70] McMahan HB, Ramage D, Talwar K, Zhang L (2017) Learning differentially private recurrent language models. arXiv:1710.06963 source
The Introduction (p. 1) identifies next-word prediction on a mobile keyboard as the paper’s motivating application. The Experimental Results (p. 6) evaluate federated training of a language model tuned for that setting, and the Conclusions (p. 9) describe its promise for real-world applications. The experiments used a public dataset, so this supports the claim of a promising application, not a deployed keyboard system. (LLM, conf=0.92)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Their results show that training the model in the federated learning framework leads to comparable performance to the traditional centralized learning setting.”
§ Federated Learning for Healthcare Informatics > Applications > Healthcare
What the sources say
- Supportedcites [90] Sharma P, Shamout FE, Clifton DA (2019) Preserving patient privacy while training a predictive model of in-hospital mortality. arXiv:1912.00354 source
The Abstract (p. 1) states that federated training achieved performance comparable to traditional centralized training. The Problem Formulation (p. 2) identifies the task as predicting in-hospital mortality for ICU patients, and the Summary (p. 4) repeats the performance comparison. (LLM, conf=0.95)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, this specific uniform distribution is not an adequate solution in most scenarios.”
§ Federated Learning for Healthcare Informatics > Federated Learning > Statistical Challenges of Federated Learning
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 19
- Probably not a substantial claim. 13
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Privacy and Security: Additional privacy protections are needed for unreliable participating clients. It is impossible to ensure all clients are equally reliable.”
§ Federated Learning for Healthcare Informatics > Federated Learning
What the sources say
- Partly supportedcites [13] Caldas S, Wu P, Li T, Konečnỳ J, McMahan HB, Smith V, Talwalkar A (2018) Leaf: a benchmark for federated settings. arXiv:1812.01097 sourceExperimental — accuracy not yet measured
The Introduction (p. 1) says federated methods must address privacy and security concerns because personally generated data is sensitive. It does not say that participating clients are unreliable, that their reliability cannot be ensured, or that unreliability calls for additional privacy protections. (LLM, conf=0.83)
- Not matchedcites [96] Smith V, Chiang CK, Sanjabi M, Talwalkar AS (2017) Federated multi-task learning. In: Advances in neural information processing systems, pp 4424–4434
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: The number of participating clients in federated learning is large, potentially thousands or millions.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Federated dropout not only reduces the downlink communication but also reduces the size of uplink updates. Moreover, the local computational costs is correspondingly reduced since the local training procedure dealing with parameters with smaller dimensions.”
§ Federated Learning for Healthcare Informatics > Federated Learning > Communication Efficiency of Federated Learning
What the sources say
- Supportedcites [12] Caldas S, Konečny J, McMahan HB, Talwalkar A (2018) Expanding the reach of federated learning by reducing client resource requirements. arXiv:1812.07210 source
Section 3.2 (p. 4) says federated dropout transmits only the coefficients needed for a smaller sub-model, reducing server-to-client communication. It explicitly states that client-to-server updates are smaller and that local training requires fewer FLOPS. (LLM, conf=0.95)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Communication Efficiency: The number of clients K is large and can be much bigger than the average number of training sample stored in the activated clients, i.e., K ≫ (n/K).”
§ Federated Learning for Healthcare Informatics > Federated Learning
What the sources say
- Supportedcites [13] Caldas S, Wu P, Li T, Konečnỳ J, McMahan HB, Smith V, Talwalkar A (2018) Leaf: a benchmark for federated settings. arXiv:1812.01097 source
Table 1 (Section 2 LEAF, p. 3) reports thousands to millions of devices for several datasets, with far fewer mean samples per device—for example, 660,120 devices and 2.42 samples per device in Sent140. The Introduction (p. 1) also describes federated settings as having very large numbers of devices. These passages support the claim that client count can greatly exceed average samples per client, though not for every dataset listed. (LLM, conf=0.91)
- Not matchedcites [96] Smith V, Chiang CK, Sanjabi M, Talwalkar AS (2017) Federated multi-task learning. In: Advances in neural information processing systems, pp 4424–4434
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Therefore, some researches began to study fully decentralized framework where the central server is not required [41, 60, 85, 91]. The local clients are distributed over the graph/network where they only communicate with their one-hop neighbors.”
§ Federated Learning for Healthcare Informatics > Federated Learning > Communication Efficiency of Federated Learning
What the sources say
- Supportedcites [41] He C, Tan C, Tang H, Qiu S, Liu J (2019) Central server free federated learning over single-sided trust social networks. arXiv:1910.04956 source
The Introduction (p. 1) describes a decentralized architecture in which the central node is removed and each node communicates only with its neighbors. It also presents the paper’s federated-learning setting as one without a central server; graph neighbors correspond to the citing sentence’s one-hop neighbors. (LLM, conf=0.94)
- Supportedcites [60] Lalitha A, Kilinc OC, Javidi T, Koushanfar F (2019) Peer-to-peer federated learning on graphs. arXiv:1901.11173 source
The Introduction (p. 1) explicitly describes a fully decentralized framework with no centralized controller and nodes that communicate only with their one-hop neighbors. The Discussion (p. 6) likewise describes learning with no central server. (LLM, conf=0.95)
- Partly supportedcites [85] Roy AG, Siddiqui S, Pölsterl S, Navab N, Wachinger C (2019) Braintorrent: a peer-to-peer environment for decentralized federated learning. arXiv:1905.06731 sourceExperimental — accuracy not yet measured
The Introduction (p. 2) and Conclusion (p. 8) describe BrainTorrent as server-less, with clients communicating directly rather than through a central server. The one-hop-neighbor restriction is not supported as a general network rule: Section 2.2 (p. 4) says every client is directly connected to every other client, and an initiating client pings all the others. (LLM, conf=0.86)
- Partly supportedcites [91] Shayan M, Fung C, Yoon CJ, Beschastnikh I (2018) Biscotti: a ledger for private and secure peer-to-peer machine learning. arXiv:1811.09904 sourceExperimental — accuracy not yet measured
The Abstract (p. 1) describes Biscotti as a fully decentralized peer-to-peer approach, supporting the claim that it does not require a central server. The Noising protocol (p. 7) says a peer contacts selected noising peers, but our copy does not establish that clients communicate only with one-hop neighbors. (LLM, conf=0.87)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Compared to AFL, Li et al. [64] proposed q-Fair Federated Learning (q-FFL), assigning higher weight to devices with poor performance, so that the distribution of accuracy in the network reduces in variance.”
§ Federated Learning for Healthcare Informatics > Federated Learning > Statistical Challenges of Federated Learning
What the sources say
- Supportedcites [64] Li T, Sanjabi M, Smith V (2019) Fair resource allocation in federated learning. arXiv:1905.10497 source
The Introduction (p. 1) says q-FFL gives higher relative weight to devices with higher loss. Section 4.2 (p. 7) reports that q-FFL with q > 0 reduces the variance of testing accuracies across devices. (LLM, conf=0.94)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Statistical Challenge: The data distribution among all clients differ greatly, i.e., , we have . It is such that any data points available locally are far from being a representative sample of the overall distribution, i.e., .”
§ Federated Learning for Healthcare Informatics > Federated Learning
What the sources say
- Partly supportedcites [13] Caldas S, Wu P, Li T, Konečnỳ J, McMahan HB, Smith V, Talwalkar A (2018) Leaf: a benchmark for federated settings. arXiv:1812.01097 sourceExperimental — accuracy not yet measured
The Introduction (p. 1) says devices have different, though perhaps related, data-generating distributions, supporting the claim of heterogeneous client data. It does not establish that every client's local samples are far from representative of the overall distribution; that absolute formulation goes beyond the passage. (LLM, conf=0.84)
- Not matchedcites [96] Smith V, Chiang CK, Sanjabi M, Talwalkar AS (2017) Federated multi-task learning. In: Advances in neural information processing systems, pp 4424–4434
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“However, DP only protects users from data leakage to a certain extent and may reduce performance in prediction accuracy because it is a lossy method [18].”
§ Federated Learning for Healthcare Informatics > Federated Learning > Privacy and Security
What the sources say
- Supportedcites [18] Cheng K, Fan T, Jin Y, Liu Y, Chen T, Yang Q (2019) Secureboost: a lossless federated learning framework. arXiv:1901.08755 source
The Introduction (p. 1) says differential privacy prevents user-data leakage only to a certain degree. It also says that adding noise makes the method lossy in machine learning and may result in unsatisfactory prediction accuracy. (LLM, conf=0.95)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Federated learning has also enabled predictive modeling based on diverse sources, which can provide clinicians with additional insights into the risks and benefits of treating patients earlier [9, 10, 90].”
§ Federated Learning for Healthcare Informatics > Applications > Healthcare
What the sources say
- Partly supportedcites [9] Boughorbel S, Jarray F, Venugopal N, Moosa S, Elhadi H, Makhlouf M (2019) Federated uncertainty-aware learning for distributed hospital ehr data. arXiv:1910.12191 sourceExperimental — accuracy not yet measured
The Introduction (p. 1) says federated learning enables predictive models trained jointly from different EHR sources. The Clinical Application section (p. 2) describes predicting preterm-birth risk three months before delivery, but does not say the model gives clinicians insight into the risks and benefits of treating patients earlier. (LLM, conf=0.88)
- Partly supportedcites [10] Brisimi TS, Chen R, Mela T, Olshevsky A, Paschalidis IC, Shi W (2018) Federated learning of predictive models from federated electronic health records. Int J Med Inform 112:59–67 sourceExperimental — accuracy not yet measured
The Abstract (Objective) and Conclusions describe a federated model that uses distributed EHR data to predict future hospitalizations. The Introduction (Health application) links prediction and early prevention, but the paper does not assess the risks and benefits of treating patients earlier or show that its model gives clinicians insight into that trade-off. Page numbers are unavailable in our copy. (LLM, conf=0.86)
- Partly supportedcites [90] Sharma P, Shamout FE, Clifton DA (2019) Preserving patient privacy while training a predictive model of in-hospital mortality. arXiv:1912.00354 sourceExperimental — accuracy not yet measured
The Introduction (p. 1) describes federated training of an in-hospital mortality model using data distributed across hospitals, and the Summary (p. 4) discusses the potential value of larger, more diverse datasets. The paper does not say that its model gives clinicians insight into the risks and benefits of treating patients earlier. Its evaluation used one database distributed among virtual workers, rather than data collected from multiple hospitals (Dataset, p. 3). (LLM, conf=0.86)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Federated learning is a viable method to connect EHR data from medical institutions, allowing them to share their experiences, and not their data, with a guarantee of privacy [9, 25, 34, 45, 65, 82].”
§ Federated Learning for Healthcare Informatics > Applications > Healthcare
What the sources say
- Partly supportedcites [9] Boughorbel S, Jarray F, Venugopal N, Moosa S, Elhadi H, Makhlouf M (2019) Federated uncertainty-aware learning for distributed hospital ehr data. arXiv:1910.12191 sourceExperimental — accuracy not yet measured
The Introduction (p. 1) says federated learning jointly trains models from different EHR sources without sharing data, and §3.2 (p. 3) says the proposed algorithm preserves data privacy by sending model updates to the data location. Those passages support the paraphrase, but not the citing sentence’s stronger claim of a privacy guarantee; the Introduction also identifies privacy as an ongoing challenge. (LLM, conf=0.82)
- Partly supportedcites [25] Duan R, Boland MR, Liu Z, Liu Y, Chang HH, Xu H, Chu H, Schmid CH, Forrest CB, Holmes JH, et al. (2020) Learning from electronic health records across multiple sites: a communication-efficient and privacy-preserving distributed algorithm. J Am Med Inform Assoc 27(3):376–385 sourceExperimental — accuracy not yet measured
The Conclusion (page not provided) says ODAL enables analysis across multiple clinical datasets without transferring patient-level data, supporting the claim that institutions can learn from EHR data without sharing those records. It defines privacy preservation in that limited sense; it does not establish the citing sentence’s broader “guarantee of privacy.” (LLM, conf=0.87)
- Partly supportedcites [34] Gruendner J, Schwachhofer T, Sippl P, Wolf N, Erpenbeck M, Gulden C, Kapsner LA, Zierk J, Mate S, Stürzl M, et al. (2019) KETOS: Clinical decision support and machine learning as a service–A training and deployment platform based on Docker, OMOP-CDM, and FHIR Web Services. PloS one 14(10):1–16 sourceExperimental — accuracy not yet measured
The Results section (Example model 3; page unavailable) describes training models across a federated hospital network while only aggregated statistics leave each institution. The Discussion likewise says patient data need not leave hospitals. These passages support privacy-preserving cross-institutional training, but they do not establish the claimed guarantee of privacy. (LLM, conf=0.86)
- Partly supportedcites [45] Huang L, Liu D (2019) Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records. arXiv:1903.09296 sourceExperimental — accuracy not yet measured
The Introduction (page unavailable) says federated learning keeps EMR data local and aggregates locally computed results, allowing institutions to learn together without sharing the records. The Conclusions (page unavailable) describe the authors’ clustering method as avoiding a privacy breach. Neither passage establishes the citing sentence’s stronger claim of a guarantee of privacy. (LLM, conf=0.84)
- Partly supportedcites [65] Li Z, Roberts K, Jiang X, Long Q (2019) Distributed learning from multiple ehr databases: Contextual embedding models for medical events. J Biomed Inform 92:103138 sourceExperimental — accuracy not yet measured
The Distributed Noise Contrastive Estimation and Privacy Protection sections describe building a model from multiple EHR databases without sharing patient data; sites share a differentially private count table instead. But the Conclusion and Discussion says the experiment used two EHR systems at one institution, not different hospitals, and calls for evaluation across hospitals. The paper describes privacy protection, not an unqualified guarantee of privacy. (LLM, conf=0.86)
- Not checked — no full text of the sourcecites [82] Raja PV, Sivasankar E (2014) Modern framework for distributed healthcare data analytics based on hadoop. In: Information and communication technology-EurAsia conference. Springer, pp 348–355 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Federated learning, a mechanism of training a shared global model with a central server while keeping all the sensitive data in local institutions where the data belong, provides great promise to connect the fragmented healthcare data sources with privacy-preservation.”
§ Federated Learning for Healthcare Informatics > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Mohri et al. [73] proposed a minimax optimization scheme, i.e., agnostic federated learning (AFL), where the centralized model is optimized for any possible target distribution formed by a mixture of the client distributions.”
§ Federated Learning for Healthcare Informatics > Federated Learning > Statistical Challenges of Federated Learning
What the sources say
- Not matchedcites [73] Mohri M, Sivek G, Suresh AT (2019) Agnostic federated learning. In: Chaudhuri K, Salakhutdinov R (eds) Proceedings of the 36th International conference on machine learning, proceedings of machine learning research, vol 97. PMLR, Long Beach, pp 4615–4625
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Apparently, we can consider three ways to reduce the communication cost: (a) reduce the number of clients K, (b) reduce the update size, (c) reduce the number of updates U.”
§ Federated Learning for Healthcare Informatics > Federated Learning > Communication Efficiency of Federated Learning
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Healthcare data, however, are usually fragmented and private making it difficult to generate robust results across populations.”
§ Federated Learning for Healthcare Informatics > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Federated learning holds great promises on healthcare data analytics.”
§ Federated Learning for Healthcare Informatics > Introduction
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Federated learning, a mechanism of training a shared global model with a central server while keeping all the sensitive data in local institutions where the data belong”
§ Federated Learning for Healthcare Informatics > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“Without well-designed incentives, self-interested mobile or other wearable devices will be reluctant to participate in federal learning tasks, which will hinder the adoption of federated learning [53].”
§ Federated Learning for Healthcare Informatics > Conclusions and Open Questions
What the sources say
- Not checked — no full text of the sourcecites [53] Kang J, Xiong Z, Niyato D, Yu H, Liang YC, Kim DI (2019) Incentive design for efficient federated learning in mobile networks: A contract theory approach. arXiv:1905.07479 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Federated tries to make isolated institutions or devices share their experiences, and the performance of machine learning model will be significantly improved by the formed large medical dataset.”
§ Federated Learning for Healthcare Informatics > Conclusions and Open Questions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Differential privacy (DP) [26] is an alternative theoretical model for protecting the privacy of individual data, which has been widely applied to many areas, not only traditional algorithms, e.g., boosting [27], principal component analysis [15], support vector machine [86], but also deep learning research [1, 70]. It ensures that the addition or removal does not substantially affect the outcome”
§ Federated Learning for Healthcare Informatics > Federated Learning > Privacy and Security
What the sources say
- Not checked — no full text of the sourcecites [26] Dwork C, Kenthapadi K, McSherry F, Mironov I, Naor M (2006) Our data, ourselves: Privacy via distributed noise generation. In: Annual international conference on the theory and applications of cryptographic techniques. Springer, pp 486–503 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, most health systems suffer from data clutter and efficiency problems.”
§ Federated Learning for Healthcare Informatics > Conclusions and Open Questions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This widespread adoption is due in part by the fact that it enables a collaborative modeling mechanism that allows for efficient ML all while ensuring data privacy and legal compliance between multiple parties or multiple computing nodes.”
§ Federated Learning for Healthcare Informatics > Applications
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The analyzed results are apparently worthless when dirty data are accidentally used as samples.”
§ Federated Learning for Healthcare Informatics > Conclusions and Open Questions
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Medical devices in hospitals have incomparable advantages over wearable devices.”
§ Federated Learning for Healthcare Informatics > Conclusions and Open Questions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Although SMC guarantees that none of the parties shares anything with each other or with any third party, it can not prevent an adversary from learning some individual information”
§ Federated Learning for Healthcare Informatics > Federated Learning > Privacy and Security
What the sources say
- Not checked — no full text of the sourcecites [8] Bonawitz K, Ivanov V, Kreuter B, Marcedone A, McMahan HB, Patel S, Ramage D, Segal A, Seth K (2017) Practical secure aggregation for privacy-preserving machine learning. In: Proceedings of the 2017 ACM SIGSAC conference on computer and communications security. ACM, pp 1175–1191 source
- Not matchedcites [78] Pathak M, Rane S, Raj B (2010) Multiparty differential privacy via aggregation of locally trained classifiers. In: Advances in neural information processing systems, pp 1876–1884
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Federated learning has the potential to connect all the isolated medical institutions, hospitals, or devices to make them share their experiences with privacy guarantee.”
§ Federated Learning for Healthcare Informatics > Conclusions and Open Questions
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, the prediction task is currently restricted and relatively simple.”
§ Federated Learning for Healthcare Informatics > Conclusions and Open Questions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The quality of data collected from multiple sources is uneven and there is no uniform data standard.”
§ Federated Learning for Healthcare Informatics > Conclusions and Open Questions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“The creation and utility of standardized common data models, such as OMOP [44], allow for more wide-spread replication analyses but it does not overcome the limitations of joint data access.”
§ Federated Learning for Healthcare Informatics > Applications > Healthcare
What the sources say
- Not matchedcites [44] Hripcsak G, Duke JD, Shah NH, Reich CG, Huser V, Schuemie MJ, Suchard MA, Park RW, Wong ICK, Rijnbeek PR, et al. (2015) Observational health data sciences and informatics (ohdsi): opportunities for observational researchers. Stud Health Technol Inform 216:574
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
The authors state this as an assumption.
The number of participating clients in federated learning is large, potentially thousands or millions.
“In federated learning, we usually assume the number of participated clients (e.g., phones, cars, clinical institutions...) is large, potentially in the thousands or millions.”
The authors state this as an assumption.
There are K activated clients where data reside, each with a data distribution and a number of samples.
“Mathematically, assume there are K activated clients where the data reside in (a client could be a mobile phone, a wearable device, or a clinical institution data warehouse, etc.). Let denote the data distribution associated with client k and nk the number of samples available from that client.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Due to the contemporary features, DFL can be integrated with P2Pbased communication technologies such as blockchain [37] to build decentralized FL systems.”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [37]
Experimental — accuracy not yet measuredIn the citing paper
“FL enables healthcare records that are located across different institutions to be connected without revealing personal information [11].”
From A comprehensive survey on federated learning techniques for healthcare informatics (2023) · cites this paper as [11]
In the citing paper
“Xu et al. [11] surveyed the papers that focus FL in the biomedical area to provide a review.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [11]
In the citing paper
“By enabling multiple institutions to collaborate on AI model development without exchanging patient data, FL addresses the most pressing concerns in healthcare AI: privacy and data integrity.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“By enabling multiple institutions to collaborate on AI model development without exchanging patient data, FL addresses the most pressing concerns in healthcare AI: privacy and data integrity.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“As Ref. [19] explains, only the model parameters or gradients-essentially the learned features or updates to the model-are shared with a central server or among participants.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“This ensures that the raw patient data, which may contain personally identifiable information, never leaves the institution's premises where it was collected.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“The authors of [19] highlight the statistical variance in data distribution among clients, communication efficiency, and the potential for malicious clients as operational challenges in FL.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“The core principle of DP is to ensure that the presence or absence of any individual's data do not substantially alter the outcome of an analysis.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“This method is advantageous for sharing insights from health data, such as statistical summaries or AI model parameters, while safeguarding patient confidentiality.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“It functions by introducing a calculated amount of random noise, either to the data itself or to the outputs of algorithms, thereby making it statistically challenging for attackers to deduce sensitive information about any individual in the dataset.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“By creating a secure and tamper-proof ledger for medical data transactions, blockchain technology ensures the integrity and traceability of patient data, which is paramount in clinical settings.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“As privacy protection intensifies with increased noise, the usefulness of the data for research and the accuracy of AI models may be compromised.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“While it excels at enhancing privacy, it can simultaneously lead to reduced accuracy of predictions or analyses due to the added noise.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“For instance, blockchain can be used to securely manage electronic health records (EHRs), providing a reliable source of patient data for healthcare professionals and AI algorithms while preventing unauthorized access and data breaches [19].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“Blockchain technology stands out for its unique attributes of decentralization, immutability, and transparency, which are particularly beneficial in the context of healthcare AI systems.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“This hybrid approach is designed to control the escalation of noise with the number of participants, thus maintaining both privacy and the practical value of the data.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“DP's application extends to various algorithms, including but not limited to boosting, principal component analysis, support vector machines, and deep learning.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“It is also effectively employed in Federated Learning scenarios involving decentralized data processing across multiple entities, such as hospitals or research institutions, to prevent indirect data leakage-a prevalent issue in collaborative environments [19].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“By enabling the training of AI models on decentralized data sources, FL ensures that sensitive patient information remains within the confines of the local institution, thereby preserving privacy.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“To counterbalance this, researchers often integrate DP with other methods like Secure Multi-party Computation (SMC) to balance privacy and data utility [19].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“At the same time, client selection protocols, model compression, and update reduction strategies aim to improve communication efficiency.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“FL's ability to integrate data from various sources without compromising privacy is a testament to its potential to enhance the capabilities of healthcare AI systems.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“Moreover, integrating Secure Multi-party Computation and Differential Privacy within FL frameworks enhances the privacy and security of the data, making FL a powerful tool for healthcare applications [19].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“Techniques like Agnostic Federated Learning and q-Fair Federated Learning have been proposed to address the issue of data heterogeneity.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“By adopting these technologies, healthcare providers can meet the requirements of laws such as HIPAA in the United States and GDPR in Europe, which mandate strict data privacy and security standards.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“The ethical considerations of patient data use are also addressed, as these technologies provide mechanisms to ensure that patient consent is obtained and that data are used to respect individual autonomy and dignity [19].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [19]
In the citing paper
“In healthcare, AI performs a vital and crucial role in understanding the human thinking process through the computer using various effective AI-based algorithms to make complex examinations easier for healthcare professionals [83].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [83]
In the citing paper
“To overcome this issue, federated learning is used to train the shared global model using data from several institutions without compromising data privacy [13].”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [13]
In the citing paper
“However, the results of deep learning are less accurate, as the datasets for training and testing are smaller.”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [13]
In the citing paper
“It has been observed that addressing the security issues was primarily focused [11]; however, more attention needs to be paid to the protection of end-user privacy in modern electronic health care systems [12], [13].”
From Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey (2023) · cites this paper as [11]
In the citing paper
“Federated machine learning has shown promising results in the healthcare industry, as indicated in [146,147].”
From Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review (2023) · cites this paper as [146]
In the citing paper
“Xu et al. performed a survey focusing on the progress of federated learning in healthcare informatics [7].”
From Applications of Federated Learning; Taxonomy, Challenges, and Research Trends (2022) · cites this paper as [7]
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215 · cited 2×
“The naive way to solve the federated learning problem is through Federated Averaging (FedAvg) [61].”
BackgroundMethodDistilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204 · cited 1×
“, sparse, low-rank [58], or more specifically, pruning the least useful connections in a network [37, 113], weight quantization [17, 89], and model distillation [43].”
BackgroundMIMIC-III, a freely accessible critical care database
Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-W. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., & Mark, R. G. (2016) · Scientific Data · doi:10.1038/sdata.2016.35 · Cited by 8,141 · cited 1×
“tionbetweenparticipants aboutsomeoftheencryptedresults[70]. Table1:Summaryofrecentworkonfederatedlearningforhealthcare Problem MLMethod #Hospital Data PatientSimilarityLearning[54] Hashing 3 MIMIC-III[42] PatientSimilarityLearning[96] Hashing 20 MIMIC-III Phenotyping[47] *TF 1-5 MIMIC-III,UCSD[92] Phenotyping[59] NLP…”
MethodAdvances and Open Problems in Federated Learning
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.04977 · Cited by 7,857
Advances and Open Problems in Federated Learning
Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning · doi:10.1561/2200000083 · Cited by 7,772
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137 · cited 1×
“esearch[64].Whileprovidingahugeamountof patient data for analysis, EHRs contain systemic and random biases overall andspecifictohospitalsthatlimitthegeneralizabilityofresults.Forexample, Obermeyeretal.[68]foundthatacommonlyusedalgorithmtodetermineenrollment in specific health programs were biased against African America…”
BackgroundDeep Learning with Differential Privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978318 · Cited by 6,578 · cited 2×
“, boosting [27], principal component analysis [15], support vector machine [86], but also deep learning research [1, 70].”
BackgroundFederated Machine Learning: Concept and Applications
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322 · cited 3×
“privacyandlegalcompliancebetweenmultiplepartiesormultiplecomputing nodes. Some promising examples that highlight these capabilities are virtual keyboard prediction [31,62], smart retail [99], finance [97], vehicle-to-vehicle communication[80].Inthissection,wefocusprimarilyonapplicationswithin thehealthcarespace,butalso…”
BackgroundInfluentialHan, S., Mao, H., & Dally, W. J. (2015) · arXiv · doi:10.48550/arxiv.1510.00149 · Cited by 4,662 · cited 1×
“, sparse, low-rank [58], or more specifically, pruning the least useful connections in a network [37, 113], weight quantization [17, 89], and model distillation [43].”
Background
Research works printed but not linked: 5
- Kawa D, Punyani S, Nayak P, Karkera A, Jyotinagar V (2019) Credit risk assessment from combined bank records using federated learning. International Research Journal of Engineering and Technology (IRJET) 6(4):1355–1358
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- Pathak M, Rane S, Raj B (2010) Multiparty differential privacy via aggregation of locally trained classifiers. In: Advances in neural information processing systems, pp 1876–1884
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Items 1,001–1,100 of 1,496
Mapping Severity Levels of Cucumber Diseases through Federated Learning CNN
Jindal, V., Kukreja, V., Mehta, S., et al. (2023) · IEEE Global Conference for Advancement in Technology (GCAT) · doi:10.1109/gcat59970.2023.10353413 · Cited by 1
“The illnesses in this research include Pale or yellow leaf patches that eventually become brown, which are the first signs of downy mildew [10].”
BackgroundPrivate Continuous Survival Analysis with Distributed Multi-Site Data
Bonomi, L., Lionts, M., & Fan, L. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386571 · Cited by 1
“Among them, the federated § Work conducted during the Vanderbilt Biomedical Informatics Summer Program. framework has emerged as a promising solution to addressing data privacy concerns [7], [8].”
BackgroundFederated Approach to Online Pairwise Comparison Ranking Problem
Lin, T.-Y., & Tsai, Y.-L. (2024) · International Conference on Consumer Electronics · doi:10.1109/icce59016.2024.10444309 · Cited by 1
“FL has been widely applied in many fields such as healthcare [3], [4], transportation [5], [6], IoT [7], mobile applications [8], and recommendation systems [9]–[11].”
BackgroundGraphLeak: Patient Record Leakage through Gradients with Knowledge Graph
Zhang, X. S., Guan, W., Lu, J., Qiu, Z., Cheng, J., Wu, X., & Zheng, Y. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3648157 · Cited by 1
Federated Learning Implementation with Privacy Leakage Prevention for Hand-Written Digit Recognition
Priyadarsini, N. I., & Raja, G. (2024) · International journal of electrical and computer engineering systems · doi:10.32985/ijeces.15.5.4 · Cited by 2
“Jie et al. [35] stated that the proliferation of healthcare data offers significant potential for improving care, but privacy challenges and data fragmentation persist.”
Government and economic regulations on federated learning in emerging digital healthcare systems
Musa, A., Abdulfatai, A. O., Jacob, S. E., & Oluyemi, D. F. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13897-3.00012-6 · Cited by 1
A New Data-Free Knowledge Distillation Method for Non-IID Federated Learning
Huang, X. (2024) · International Conference on Communications, Information System and Computer Engineering (CISCE) · doi:10.1109/cisce62493.2024.10652604 · Cited by 1
“Federated learning is already being used successfully in areas like healthcare [1], biometrics [2], and recommender systems [3].”
MethodOdeyomi, O. T., Smith, A. C., Whitmore, A., Ige, O., & Roy, K. (2024) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom61039.2024.10773867 · Cited by 1
Refined RSA Encryption Algorithm to Safeguard Privacy in Healthcare Data
Christopher, A. B. A., Sivakumar, M., Murugavel, A. S. M., et al. (2024) · Algorithms for intelligent systems · doi:10.1007/978-981-97-5791-6_21 · Cited by 1
Vijayalakshmi, P. S., & Banupriya, G. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-7011-7.ch013 · Cited by 1
AI-Ready Multimodal Data Pipeline to Enrich Cancer Care
Rashid, R., Hashtarkhani, S., Kumsa, F. A., Chinthala, L. K., White, B. M., Zink, J. A., Brett, C. L., Davis, R. L., Schwartz, D. L., & Shaban‐Nejad, A. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825353 · Cited by 1
“Xu et al. [18] demonstrated the effectiveness of combining imaging features with clinical data, showing improved performance in diagnostic tasks.”
Federated Privacy-Preserving Visualization: A Vision Paper
Tao, Y., Sarwate, A. D., Panta, S. R., Plis, S., & Calhoun, V. D. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825849 · Cited by 1
Privacy-Preserving Secure Distributed Computer Vision for Malaria Cells Detection
Arévalo, I., Salmeron, J. L., & Oliva, I. D. L. (2024) · International Conference on Federated Learning Technologies and Applications (FLTA) · doi:10.1109/flta63145.2024.10839973 · Cited by 2
“Xu et al. [19] offer an overview of common solutions to address statistical challenges, system challenges, and privacy issues in federated learning, and highlight the potential implications and opportunities for the healthcare sector.”
A systematic review of the foundations, applications, and challenges of federated learning
Fateh, H., Rezvani, M., & Tahanian, E. (2024) · Archives de l'Institut d'Hessarek. · doi:10.61186/jsdp.21.3.23 · Cited by 1
Agarwal, S., Khedkar, S., Subramanian, S., & Tripathy, B. K. (2024) · Medical virology · doi:10.1007/978-981-97-2938-8_9 · Cited by 1
FedSSA: Reducing Overhead of Additive Cryptographic Methods in Federated Learning With Sketch
Ou, Z., Han, S., Zeng, Q., & Huang, Q. (2024) · IEEE International Conference on Network Protocols (ICNP) · doi:10.1109/icnp61940.2024.10858511 · Cited by 1
“It has been widely applied in various domains, including intelligent personal assistants [1], healthcare [2], and financial support [3].”
I. Federated Learning: Pushing Forward Machine Learning Without Scarifising Privacy
Ghade, O., & Narad, S. (2024) · International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA) · doi:10.1109/icaiqsa64000.2024.10882458 · Cited by 1
General Dynamic Regularization Federated Learning with Hybrid Sharpness-Aware Minimization
Zhang, F., Li, D., Lai, J., et al. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890601 · Cited by 1
GHEFL: Grouping Based on Homomorphic Encryption Validates Federated Learning
Kang, Y., Tan, W., Fan, L., et al. (2025) · Future Internet · doi:10.3390/fi17030128 · Cited by 1
Adaptive Differential Privacy Cox-MLP Model Based on Federated Learning
Niu, J., He, R., Zhou, Q., Li, W., Jiang, R., Li, H., & Chen, D. (2025) · Mathematics · doi:10.3390/math13071096 · Cited by 1
Requirements for a Federated Learning System to Strengthen IT Security in Human Resource Management
Kolb, L., Rudel, S., & Lechner, U. (2025) · Lecture notes in information systems and organisation · doi:10.1007/978-3-031-80122-8_1 · Cited by 1
Local Drift Correction and Attention Aggregation for Self-Organized Federated Learning
Liu, H., Gu, R., Wang, J., Zhang, X., Zhang, B., & Li, X. (2024) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom63139.2024.00123 · Cited by 1
“Traditional centralized data pro-This cessing methods, particularly in sensitive sectors like health-care [1], [2] and financial services [3], [4], are susceptible to data and privacy breaches.”
Falafels: A Tool for Estimating Federated Learning Energy Consumption via Discrete Simulation
de Barochez, A. M. H., Plassart, S., & Monnet, S. (2025) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-87769-8_13 · Cited by 1
Salim, S., Moustafa, N., & Turnbull, B. (2025) · International Journal of Web Information Systems · doi:10.1108/ijwis-04-2024-0120 · Cited by 7
Mitigating DDoS attacks on the internet of things using federated learning
Shirvani, G., Ghasemshirazi, S., & Azgomi, M. A. (2025) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-025-02001-z · Cited by 3
“One of the critical challenges in implementing hybrid approaches in IoT environments is managing the computational resources required for these systems [56, 57].”
Bensiah, O. A., & Benaboud, R. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-90758-6_15 · Cited by 1
Efficient verifiable secure aggregation protocols for federated learning
Xu, B., Wang, S., & Tian, Y. (2025) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2025.104161 · Cited by 2
Integrating IoMT and Federated Learning for Advanced Healthcare Monitoring in Healthcare 5.0
Almogadwy, B. S., & Alqarafi, A. (2025) · Current Pharmaceutical Biotechnology · doi:10.2174/0113892010380715250728143022 · Cited by 1
Shakkeera, L., Chen, T., Sathiyanarayanan, M., Fathima, G. M., & Vali, Y. S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-5754-4_39 · Cited by 1
Emerging Trends in Early Dementia Diagnosis: An Analysis on Advanced Machine Learning Approaches
Gami, B., Agrawal, M., & Katarya, R. (2025) · ACM Computing Surveys · doi:10.1145/3764578 · Cited by 1
“Xu et al. [147] provide a comprehensive review of federated learning in healthcare.”
Shi, Z., Gong, D., & Yan, X. (2025) · Lecture notes in operations research · doi:10.1007/978-981-96-9697-0_77 · Cited by 1
FedPLDSE: Submodel Extraction for Federated Learning in Heterogeneous Smart City Devices
Hou, X., Wang, Z., Wang, X., & Zhao, J. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9090226 · Cited by 1
“Significant promise has been demonstrated in supporting smart city functions including autonomous mobility [9–12], natural language processing [13–15], healthcare [16], and IoT-enabled services [17,18].”
kani, J. S. N., & Senthilselvi, A. (2025) · Iranian Journal of Science and Technology Transactions of Electrical Engineering · doi:10.1007/s40998-025-00877-4 · Cited by 3
Dynamic weight federated client selection using adaptive genetic algorithm
Lin, Y., Zhao, X., & Yin, Z. (2025) · Cluster Computing · doi:10.1007/s10586-025-05492-w · Cited by 1
“In addition, federated learning integrates data from multiple sources, facilitates collaboration among healthcare organizations [4], improves the accuracy of diagnosis and treatment [5], and provides more personalized healthcare services to patients [6].”
Enhancing Data Security and Efficiency in Federated Learning Through Hybrid AES-RSA Encryption
Murugesan, M., Arunprakash, V., Shankar, S., et al. (2025) · doi:10.1109/icctdc64446.2025.11158824 · Cited by 1
FedShield: A Federated Learning Approach for Secure Heart Disease Monitoring
Khatril, K., Arpita, Manikanta, M., & Verma, K. (2025) · International Conference on Computing Technologies & Data Communication (ICCTDC) · doi:10.1109/icctdc64446.2025.11158020 · Cited by 1
“FL has gained significant traction in healthcare, with successful applications such as multi-institutional brain tumor segmentation and chronic disease prediction [7], [8].”
Alnami, H. (2025) · International Conference on Computer Technology Applications (ICCTA) · doi:10.1109/iccta65425.2025.11166602 · Cited by 1
KeTS: Kernel-Based Trust Segmentation Against Model Poisoning Attacks
Gangwal, A., Conti, M., & Pauselli, T. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-07884-1_7 · Cited by 1
Asynchronous Federated Learning With Nonconvex Client Objective Functions and Heterogeneous Dataset
Forootani, A., & Iervolino, R. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3620774 · Cited by 1
“This property makes FL particularly appealing in domains where privacy and data sovereignty are paramount, such as healthcare [3], finance [4], renewable energies [5], and mobile computing [6].”
Wang, H., Wu, J., Kong, D., et al. (2025) · doi:10.1145/3746259.3760431 · Cited by 1
Maussion, C., Coindre, J., Blay, J., Schutte, K., Camara, A., Loarer, F. L., Perret, R. E., Jean‐Denis, M., Meurgey, A., Ducimetière, F., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-20495-8 · Cited by 1
Falahati, S., Alizadeh, M., Ghazipour, F., Safahi, Z., Khaledian, N., & Salmanpour, M. R. (2025) · Journal of Advances in Information Technology · doi:10.12720/jait.16.10.1364-1378 · Cited by 1
“The system presented in this study builds on these foundations by integrating state-of-the-art AI models and prioritizing user privacy and accessibility [15].”
Data integration and data fusion approaches in self-driving labs: A perspective
Gulyuk, A. V., Zaid, N. A., Chirkova, R. Y., & Yingling, Y. G. (2025) · APL Machine Learning · doi:10.1063/5.0283450 · Cited by 1
Advancing AI Integration in Healthcare Using Federated Learning
Singh, M., Kaur, G., Saxena, P., & Sharma, M. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-3306-9.ch002 · Cited by 1
Few‐shot CBCT‐based synthetic CT generation with denoising diffusion probabilistic model
Yeap, P. L., Du, X., Zhou, M., Hoole, A. C. F., Barnett, G. C., & Jena, R. (2025) · Medical Physics · doi:10.1002/mp.70126 · Cited by 1
“While data federation methods had allowed localised training without sharing of patient data, 27,28 few studies had made use of large multi-centre datasets for sCT generation.”
Enhancing privacy-preserving load forecasting in smart grids using residual multi-layer perceptron
Yogarajan, G., Abirami, K., Ahmed, K., et al. (2025) · Electrical Engineering · doi:10.1007/s00202-025-03450-8 · Cited by 1
“For instance, a system for secure healthcare analytics has been developed, using HE to encrypt patient data and DP to protect model updates during training [23].”
Samir, M., Nassrullah, A. H. A., Karaghool, A. R. M., Alshareefi, H., Asaad, A. Y. A., Mohammed, D. T., Khider, A. I., & Miah, A. S. M. (2025) · International Conference on Cyber Resilience (ICCR) · doi:10.1109/iccr67387.2025.11292550 · Cited by 1
FedPN: Lightweight Privacy-Preserving Federated Learning With Hardness of Learning Periodic Neurons
Yang, W., Xing, H., Zhang, Z., Gu, H., Fan, L., & Cao, X. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3609663 · Cited by 2
“These advantages have contributed to its widespread adoption across diverse domains, including healthcare, finance, intelligent transportation and IoT [3], [4].”
High-Order Progressive Trajectory Matching for Medical Image Dataset Distillation
Dong, L., Bian, J., Hou, J., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05185-1_27 · Cited by 1
“While approaches like federated learning [18,25,21] address privacy concerns, they demand substantial computational resources and intricate coordination among participating institutions.”
Self-citationSecure distributed multiple imputation enables missing data inference for private data proprietors
Smajlović, H., Lian, Y., Long, Q., Numanagić, I., & Jiang, X. (2026) · npj Digital Medicine · doi:10.1038/s41746-025-02271-0 · Cited by 1
“It also minimizes the need to share sensitive protected health information, complying with legal requirements and improving the privacy and security of healthcare data 14,15 .”
Federated Learning and Blockchain for Predictive Healthcare
Baranidharan, A., & P.Velmurugadoss (2025) · IEEE International Conference on Computing, Communication and Automation (ICCCA) · doi:10.1109/iccca66364.2025.11325360 · Cited by 1
“Ma et al. [7]: The authors present a privacy-preserving outsourced disease prediction model based on the random forest algorithm.”
PBA: Persistent backdoor attack on federated learning via distributed generative triggers
Yang, W., Wu, W., Liu, F., Ma, L., Zhang, Z., & Yang, B. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112038 · Cited by 1
Akbulut, S., & Çolak, C. (2026) · World Journal of Gastrointestinal Oncology · doi:10.4251/wjgo.v18.i2.113870 · Cited by 3
Privacy-preserving federated prediction of health outcomes using multi-center survey data
Das, S., Rafiei, M., Kammer, P., Skou, S. T., Grønne, D. T., Roos, E. M., Hajek, A., König, H. H., Ullah, M. S., Probul, N., et al. (2026) · BMC Medical Research Methodology · doi:10.1186/s12874-026-02785-5 · Cited by 2
“The efficacy of federated learning has been studied on image data [24], multi-omics data [26, 27], and other biomedi-cally relevant data types [23, 28, 29].”
Future Trends in Federated Learning: Enabling Secure and Personalized Healthcare Solutions
Baghel, R. S., & Mamodiya, U. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-03985-9_40 · Cited by 1
Federated learning for prenatal detection of interrupted aortic arch using fetal ultrasound imaging
Han, J., Wang, H., Feng, Y., Yang, Q., Li, J., Zhang, H., He, Y., Liu, J., Nakamura, T., Cao, Y., et al. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109795 · Cited by 1
A call for shared digital infrastructure in travel medicine: the travel health data commons
Colubri, A., Hedrich, N., Leva, J., et al. (2026) · Journal of Travel Medicine · doi:10.1093/jtm/taag013 · Cited by 1
Explainable Federated Learning for ICU Deterioration Prediction with SHAP and Attention
Nallani, V. B., Deverasetty, P., Malisetti, N., Merugu, R. R., Maganti, R. M. R., & Babu, R. K. (2025) · IEEE Pune Section International Conference (PuneCon) · doi:10.1109/punecon67554.2025.11378864 · Cited by 1
“Federated learning (FL) addresses these barriers by enabling hospitals to collaboratively train models without exchanging raw data [5], [6].”
Kempter, S., Brunner, J. O., Hanses, F., Spinner, C. D., Zabel, L. T., Römmele, C., Borgmann, S., Vehreschild, J. J., & Bartenschlager, C. C. (2026) · Health Care Management Science · doi:10.1007/s10729-025-09752-4 · Cited by 1
“The relatively new research domain, which was introduced by Google in 2016 [11], ensures data privacy and governance through decentralized training and subsequent consolidation of ML models.”
Alvarenga, L. F., Ferreira, N., Peres, A. S. C., Moioli, R. C., & Rennó‐Costa, C. (2026) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2026.3677593 · Cited by 3
“Federated Learning (FL) has emerged as a powerful paradigm for addressing this tension [7], [8], [9], [10].”
Federated Learning in Privacy Preservation and Security Enhancement for e-Healthcare Systems
Baidya, A., Jha, R. K., Modak, S., & Ghosh, S. (2026) · Springer optimization and its applications · doi:10.1007/978-3-032-15092-9_13 · Cited by 1
Wu, X., He, Y., Wang, T., & Cao, B. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132322 · Cited by 1
Wu, D., Zhong, S., He, Y., Luo, X., & Gao, X. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2026.3683950 · Cited by 4
“The FL techniques have been adopted in many applications [20], [21], [22].”
Iqbal, S., Chen, J., Pal, D., & Shen, B. (2026) · Drug Discovery Today · doi:10.1016/j.drudis.2026.104687 · Cited by 1
Federated Continuous Category Discovery and Learning
Wang, L., Liu, C., Guo, J., Ye, Q., Huang, H., Hu, H., & Dong, W. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00234 · Cited by 1
“For example, FL-powered diagnostic models in healthcare must continuously adapt to the emergence of new diseases and rare conditions to maintain their effectiveness [43, 55].”
Enhancing Privacy-Preserving Healthcare Analytics via Quantum-Inspired Federated Optimization
Mithaguru, Rakshita, R., Dhanaraj, J. A., et al. (2026) · doi:10.1109/nqcomp68334.2026.11497657 · Cited by 1
Aarthi, S., & Gohil, J. A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-8889-2.ch013 · Cited by 1
Vimalapriya, M. D., Mythili, R., Poonguzhali, A., Manikandan, G., Alabdulatif, A., & Yasmin, J. H. J. (2026) · SN Computer Science · doi:10.1007/s42979-026-05007-3 · Cited by 1
PANDIA: Personalized neuro-symbolic multimodal fusion for interpretable neonatal pain assessment
Othmani, O. E., & Naouali, S. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001442 · Cited by 1
“Federated learning enables collaborative model training while preserving data privacy [20], with applications in medical imaging [21] and clinical prediction [22].”
Privacy preserved federated deep learning for disease classification of patients medical images
Dachepalli, V., & Gavini, S. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115512 · Cited by 1
Kalinichev, A. V., Lucani, D. E., & Koren, K. (2026) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2026.118969 · Cited by 2
Federated brain tumor classification via gated dual-stream interaction and data-free distillation
Makkar, S., Deepak, S., Varghese, A., & Ameer, P. M. (2026) · International Journal of Computers and Applications · doi:10.1080/1206212x.2026.2693078 · Cited by 1
Kim, Y., Kim, J., Cho, Y., & Choi, M. (2026) · BMC Nursing · doi:10.1186/s12912-026-04949-6 · Cited by 1
A Secure CDM-Based Data Analysis Platform (SCAP) in Multi-Centered Distributed Setting
Jeon, S., Shin, C., Ko, E., & Moon, J. (2021) · Applied Sciences · doi:10.3390/app11199072 · Cited by 1
“Joint techniques for the learning of artificial intelligence models in a distributed environment without exposing data outside the institution have been actively studied [25–27].”
BackgroundTowards a Model for Inclusive Healthcare Access post COVID-19
Bhatt, P., Akanfe, O., Guo, Y., & Gong, Y. (2022) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2022.455 · Cited by 1
Owoyemi, A., & Oyewusi, W. (2022) · Artificial Intelligence in Medicine · doi:10.1007/978-3-030-64573-1_331 · Cited by 1
Trends in using IoT with machine learning in smart health assessment
Rath, S. (2022) · International Journal of Health Sciences · doi:10.53730/ijhs.v6ns3.6404 · Cited by 1
“Automatic patient and illness monitoring saves time and can step in when all doctors are elsewhere busy, as is the case in an emergency [66].”
BackgroundIslam, H., Alaboud, K., Paul, T., Rana, M. K. Z., & Mosa, A. S. M. (2022) · PubMed Central · Cited by 2
Brännvall, R., Linge, H. M., & Östman, J. (2023) · Linköping electronic conference proceedings · doi:10.3384/ecp199006 · Cited by 1
“For technical reviews of the current and future applications of federated learning for biomedical data, see for example [7]–[9].”
BackgroundNonhypothesis-Driven Research: Data Mining and Knowledge Discovery
Cummins, M. R., Nachimuthu, S., Abdelrahman, S., Facelli, J. C., & Gouripeddi, R. (2023) · Health informatics · doi:10.1007/978-3-031-27173-1_20 · Cited by 4
Khaerul, K., & R, M. A. H. (2023) · El-Hekam · doi:10.31958/jeh.v8i1.9417 · Cited by 4
Federated transfer learning: Concept and application
Ni, T. (2023) · Applied and Computational Engineering · doi:10.54254/2755-2721/4/20230487 · Cited by 1
Dong, X., Shi, Z., Huang, X., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-4749-2_27 · Cited by 1
Visually Analysing the Fairness of Clustered Federated Learning with Non-IID Data
Li, H., Cui, W., Zhu, B., & Zhang, H. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191762 · Cited by 2
“…to its privacy-preserving [3], reduced costs [4], and possibly higher efficiency [5], FL has attracted considerable research attention and exhibited great promise in various applications including autonomous driving [6], wireless communication [7], healthcare [8] as well as sales and finance [9].”
BackgroundA Novel Federated Learning with Bidirectional Adaptive Differential Privacy
Li, Y., Xu, J., Zhu, J., et al. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-5968-6_23 · Cited by 1
Self-citationBreakthroughs and Challenges in Multimedia Privacy and Security in the Internet of Things (IoT)
Nisa, R., & Baba, A. M. (2023) · Springer eBooks · doi:10.1007/978-3-031-34873-0_3 · Cited by 1
Enhancing generalization robustness of federated learning in highly heterogeneous environments
Wei, W. A. N., Shengshan, H. U., Jianrong, L. U., Minghui, L. I., Ziqi, Z. H. O. U., & Hai, J. I. N. (2023) · Scientia Sinica Informationis · doi:10.1360/ssi-2023-0107 · Cited by 1
Non-Cryptographic Privacy Preserving Machine Learning Methods: A Review
Şahinbaş, K., Çatak, F. Ö., Kuzlu, M., Tabassum, M., & Sarp, S. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-50920-9_32 · Cited by 1
Floreani, E. D., & Chau, T. T. (2023) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53992.2023.10394136 · Cited by 1
“Sensitive user data never have to leave the individual repository or device from which they were generated, making federated learning an increasingly attractive option for machine learning applications in healthcare [7].”
BackgroundPoisoning Detection in Federated Learning System: An Adaptive Approach
Cheng, A., Qi, L., Lv, L., Gao, S., & Li, Z. (2023) · International Conference on Intelligent Communication and Networking (ICN) · doi:10.1109/icn60549.2023.10426100 · Cited by 1
“Distributed machine learning(DML) can be used to solve the problem of data islanding [1], for example, a federal learning approach can be used to train a medical AI model while safeguarding the privacy and security of various medical data holders [2] [3].”
MethodLeveraging Cloud Computing for Healthcare
Mittal, P., Ansari, N., Dadhich, A., & Ajmera, P. (2023) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-2639-8.ch013 · Cited by 1
Fairness in federated learning
Lin, X., Xu, X., Wu, Z., et al. (2024) · Elsevier eBooks · doi:10.1016/b978-0-44-319037-7.00016-8 · Cited by 1
Gao, C. (2024) · Journal of Electrical Systems · doi:10.52783/jes.3990 · Cited by 1
“However, as more users become aware of data security and privacy[4] issues, data centralization faces significant challenges[5].”
BackgroundFSVNet: Federated Self-driving Vehicle Network based on decomposition global convergence
Bonyani, M., & Rad, A. B. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.05.059 · Cited by 1
Kang, B., Zhang, N., & Zhu, J. (2024) · IET Blockchain · doi:10.1049/blc2.12075 · Cited by 1
Ramanathan, M., Sundaram, P. M., Kumar, S. S. S., & Devi, M. K. K. (2024) · International Conference on Computational Intelligence and Communication Technologies (CCICT) · doi:10.1109/ccict62777.2024.00057 · Cited by 3
Research on federal learning privacy protection based on secure multi-party computing
Liu, B., & Blancaflor, E. (2024) · International Conference on Cyber Security, Artificial Intelligence and Digital Economy (CSAIDE) · doi:10.1145/3672919.3672947 · Cited by 1
HaLo-FL: Hardware-Aware Low-Precision Federated Learning
Venkatesha, Y., Bhattacharjee, A., Moitra, A., & Panda, P. (2024) · Design, Automation, and Test in Europe · doi:10.23919/date58400.2024.10546513 · Cited by 1
Gottardelli, B., Gatta, R., Nucciarelli, L., et al. (2024) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi61247.2024.00053 · Cited by 1
“In recent years, Distributed data analytics has gained popularity due to its capability to conduct data analysis in a distributed fashion, enabling multi-party cooperation in collaborative research effort without requiring data relocation [3].”
BackgroundDecentralized Diagnostics: The Role of Federated Learning in Modern Medical Imaging
Rahmaniar, W., Deng, Z., Yang, Y., Jin, Z., & Suzuki, K. (2024) · Intelligent systems reference library · doi:10.1007/978-3-031-65640-8_9 · Cited by 1
Metadata
- Authors
- Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F.
- Affiliations
- Weill Cornell Medicine (US)
- Icahn School of Medicine at Mount Sinai (US)
- United States Department of Defense (US)
- University of Florida (US)
- Corresponding author
- Wang, Fei
- Venue
- Journal of Healthcare Informatics Research
- Published
- 2020-11-12
- Volume
- 5
- Issue
- 1
- Pages
- 1-19
- ISSN
- 2509-4971 (ISSN-L)
- 2509-498X
- PMC
- PMC7659898
- OpenAlex
- W3100779497
- Cited by
- 1,542
- References
- 130
- Citations by year
- 2026: 239 · 2025: 366 · 2024: 314 · 2023: 278 · 2022: 204 · 2021: 111 · 2020: 29
- FWCI
- 61.09
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Healthcare Informatics Research (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1750326)
- National Institute on Aging (P30AG028740)
- Office of Naval Research (N00014-18-1-2585, N00014-18, N00014)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- federated learning, healthcare data privacy, electronic health records, data fragmentation, privacy-preserving machine learning, biomedical applications
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-09-28, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-09-28 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
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