The DOI matches its Crossref record; 116 of the 116 references it cites were found in a registry or an index; 2 of the 99 sources its claims cite were checked for support.
Handling Privacy-Sensitive Medical Data With Federated Learning: Challenges and Future Directions
Open accessAbstract
Recent medical applications are largely dominated by the application of Machine Learning (ML) models to assist expert decisions, leading to disruptive innovations in radiology, pathology, genomics, and hence modern healthcare systems in general. Despite the profitable usage of AI-based algorithms, these data-driven methods are facing issues such as the scarcity and privacy of user data, as well as the difficulty of institutions exchanging medical information. With insufficient data, ML is prevented from reaching its full potential, which is only possible if the database consists of the full spectrum of possible anatomies, pathologies, and input data types. To solve these issues, Federated Learning (FL) appeared as a valuable approach in the medical field, allowing patient data to stay where it is generated. Since an FL setting allows many clients to collaboratively train a model while keeping training data decentralized, it can protect privacy-sensitive medical data. However, FL is still unable to deliver all its promises and meets the more stringent requirements (e.g., latency, security) of a healthcare system based on multiple Internet of Medical Things (IoMT). For example, although no data are shared among the participants by definition in FL systems, some security risks are still present and can be considered as vulnerabilities from multiple aspects. This paper sheds light upon the emerging deployment of FL, provides a broad overview of current approaches and existing challenges, and outlines several directions of future work that are relevant to solving existing problems in federated healthcare, with a particular focus on security and privacy issues.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This review surveys federated learning (FL) for privacy-sensitive medical data and the Internet of Medical Things (IoMT), covering applications, security/privacy countermeasures, and open challenges. It argues FL can protect patient data while enabling collaboration, but practical issues such as non-IID data, communication overhead, stragglers, and constrained devices remain unresolved.
Key findings
- FL enables collaborative model training while keeping patient data local; a general FL-trained model can achieve performance comparable to centrally hosted datasets and superior to models trained on isolated single-institutional data.
- Reviewed IoMT/healthcare applications include electronic health record patient similarity, hospitalization prediction, activity recognition, teledermatology, COVID-19 detection, and drug discovery (the Melloddy project applies multi-task FL to datasets from 10 pharmaceutical companies).
- Reported quantitative results include: IID data difference between FL and non-FL within 3%; PerFit accuracy 11.12% higher than classical FL; non-IID data caused up to 50% accuracy reduction; Federated Dropout reduced local computations by 1.7× without affecting accuracy; compression reduced communication costs by two orders of magnitude.
- Security and privacy risks remain despite FL, including gradient-based reconstruction, model inversion, and adversarial attacks; countermeasures include secure aggregation, differential privacy, blockchain, and homomorphic encryption, with trade-offs between security and model accuracy or system efficiency.
Methods
- Narrative review/survey design; no primary experiments or new dataset are collected.
- Synthesizes existing literature on FL concepts, IoMT applications, security/privacy concerns, and practical challenges.
- Uses summary tables (Table I on related reviews; Table II on FL-IoMT approaches) and discusses representative works across healthcare and IoMT scenarios.
Limitations
- The paper does not explicitly state limitations of its own review.
- Evident from the text: as a survey, it provides no new experimental validation; performance numbers are drawn from cited studies.
- Evident from the text: no systematic search protocol, inclusion criteria, or meta-analysis are described.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
83 Not checked yet · 42 Own interpretation · 19 No basis given · 1 Supported · 1 Partly supported · 1 Own result · accuracy not yet measured
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, as shown in Table II, the majority of the works have considered the CNN model and its variations, such as ResNet and VGG.”
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.
In the paper
“The results show that the accuracy of PerFit is 11.12% higher than that of classical FL.”
What the sources say
- Not checked yetcites [45] source
plainstates a cause- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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In the paper
“The experimental results demonstrate that FL can achieve comparable performance to the methods without FL under these settings without sacrificing the privacy of the end-users.”
What the sources say
- Not checked yetcites [50] source
boosted- Asserted: passes — The authors put it forward as true.
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In the paper
“Also, based on [43], transferring the knowledge in distributed healthcare system provides a high-quality personalized model.”
What the sources say
- Not checked yetcites [43] source
plainstates a cause- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a 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
“Privacy problem: This type of problem, even more severe than the security one, arises when vulnerabilities cause user data leakage, as it weakens the basics of FL that are designed explicitly for privacy preservation across multi-device ML.”
plainstates a cause- Asserted: passes — The authors put it forward as true.
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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
“For instance, if messages carrying the global model and local gradient updates exchanged between the central server and a local device are intercepted, gradient-based reconstruction attack algorithms can be applied to recover the raw data in the local device.”
plainstates a cause- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“In particular, private data can be extracted indirectly even from the shared information, by means of some emerging techniques, such as model inversion of the model updates [56], gradients themselves [57] and adversarial attacks [58].”
What the sources say
plainstates a cause- Asserted: passes — The authors put it forward as true.
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In the paper
“The main drawback of the resulting method, however, is the significant extra communication cost.”
What the sources say
- Not checked yetcites [62] source
plainstates a cause- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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In the paper
“For instance, Carlini et al. [59] demonstrate that it can be extracted sensitive text patterns, e.g., a specific credit card number, from a recurrent neural network trained on users' language data.”
What the sources say
- Not checked yetcites [59] Carlini, 2019, The secret sharer: Evaluating and testing unintended memorization in neural networks
boostedstates a cause- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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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
“An adversary can observe changes over time, or specific model updates, i.e., updates of a single agent, to reverse engineer and obtain some knowledge about data.”
plainstates a cause- Asserted: passes — The authors put it forward as true.
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- It may state two separate claims as one. 74
- Probably not a substantial claim. 87
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“It also improves patient privacy and reduces low latency during the collaboration of multiple hospitals/entities [7].”
What the sources say
- Partly supportedcites [7] sourceExperimental — accuracy not yet measured
In “The promise of federated efforts” (page unavailable), the paper explains that collaborating institutions do not directly access one another’s patient data, supporting the privacy claim. “Challenges and considerations” discusses higher-throughput networks and reducing idle time, but does not say FL reduces latency during collaboration; the latency part is unsupported. (LLM, conf=0.88)
plainstates a cause- Asserted: passes — The authors put it forward as true.
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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
“For example, [76] presents TrustFed, which uses Industrial IoT devices as federated learning candidates and smart contract technology over the Ethereum blockchain to maintain participants' reputations.”
What the sources say
- Supportedcites [76] source
The introduction describes IIoT devices acting as federated-learning servers or model trainers. The On-Chain FL Components section says a reputation smart contract calculates training devices’ scores, and the Results and Discussion section describes the contracts’ operation on the Ethereum blockchain. Page numbers are unavailable in our copy. (LLM, conf=0.91)
plainstates a cause- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- 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
“Although FL has been well studied in different domains, to the best of our knowledge, no existing work extensively reviews the use of FL in IoMT networks and applications.”
hedged- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Independently of the FL training strategy, a recent analysis for the medical field has shown that a general FL-trained model can achieve performance levels comparable to the ones trained on centrally hosted datasets and superior to models that only see isolated single-institutional data [27], [28].”
plainstates an association- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“With FL, a global model is trained collaboratively by each agent of the system (e.g., hospitals, IoMTs, or health care centers) over the decentralized network.This is done via local updates, without exchanging private data.”
What the sources say
- Not checked yetcites [7] source
plainstates a cause- Asserted: passes — The authors put it forward as true.
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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
“FL also requires computationally robust devices.”
plainstates a cause- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“It also shows that the TFL framework increases the classification time.”
What the sources say
- Not checked yetcites [44] source
plainstates a cause- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The experiment results demonstrate that FedHealth can improve the classification performance compared to the non-federated model and the traditional machine learning model.”
What the sources say
- Not checked yetcites [43] source
boostedstates a cause- Asserted: passes — The authors put it forward as true.
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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
“Although FL is the enabling technology in this scenario and provides a privacy-preservation capability by allowing the clients to keep the data on local devices, there are still model security and data leakage risks that would compromise the security of the FL system and the data privacy of clients.”
hedgedstates a cause- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: Security problems in FL are primarily caused by curious or malicious attackers targeting vulnerabilities of the FL system, which can lead to significant performance drop and sometimes model invalidation.
- Rests on an assumption: In healthcare and IoMT scenarios, an attacker can directly manipulate the model and data of a local affiliation, resulting in a malicious update of the global model or mislabeled data.
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
“Moreover, recently FL has made a huge contribution to the fight against COVID-19 without sharing patient data.”
boostedstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
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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.
In the paper
“Also, the authors showed that when the training data is IID, the difference between FL and non-FL is within 3%.”
What the sources say
- Not checked yetcites [37] source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Their experimental results show that human activity recognition with semi-supervised FL is not affected by the non-IID data and can achieve comparable accuracy to that of the supervised FL.”
What the sources say
- Not checked yetcites [40] Zhao, 2020, Semi-supervised federated learning for activity recognition
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Then, they study the trade-off between the communication cost and the model accuracy, which indicates that FL requires less communication and computational resources while using less complex models but at the cost of lower accuracy.”
What the sources say
- Not checked yetcites [37] source
hedgedstates a cause- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The results demonstrate that the FTL outperforms the non-TFL approach in terms of the Area Under The Curve (AUC) metric and maintains the same accuracy.”
What the sources say
- Not checked yetcites [44] source
boostedstates a cause- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. 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
“From the presented state-of-the-art on FL-IoMT, we can notice that FL plays an important role in facilitating healthcare services.”
plainstates a cause- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence.
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
“For this reason, some IoMT devices offload their model training task to the edge gateways; therefore, helping the FL training process to take advantage of the cloud and edge computing, and using the IoMT devices as simple data collectors.”
plainstates a cause- Asserted: passes — The authors put it forward as true.
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In the paper
“It can be learned from [37] that FL requires less communication overhead, and hence it can replace traditional learning approaches in IoMT applications.”
What the sources say
- Not checked yetcites [37] source
plainstates a cause- Asserted: passes — The authors put it forward as true.
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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
“Although the efficiency of FL application for healthcare systems, the shared global model trained on the FL server fails in personalization due to the different characteristics or daily activity patterns of users; hence, it is important to have a fine-grained or personalized model.”
plainstates a cause- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. 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
“Since federated learning comes with a privacy-preserving attribute, it can play a significant role in various industry domains that involve sensitive personal data, such as IoMT for healthcare.”
plainstates a possible cause- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
- Rests on an assumption: Medical data is highly sensitive and must be protected by appropriate confidentiality procedures.
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
“FL, despite a different training process compared to traditional ML, still suffers from information leakage issues and, having multiple parties, extends the attack surface.”
plainstates a cause- Asserted: passes — The authors put it forward as true.
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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
“For example, along with the data privacy and delocalization problems, FL also solves the problem of having inadequate data by providing a trust factor between heterogeneous domains.”
plainstates a cause- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“As shown in Fig. 2, edge computing helps to train the model closer to the endusers and hence may conduct inference much faster than in the cloud [46].”
What the sources say
- Not checked yetcites [46] source
hedgedstates a possible cause- Asserted: passes — The authors put it forward as true.
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In the paper
“The experimental results show that ResNet-18 has the best performance in training both with FL and without FL approaches, while ResNeXt shows the highest efficiency on images with COVID-19 labels.”
What the sources say
- Not checked yetcites [49] source
plain- Asserted: passes — The authors put it forward as true.
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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
“In IoMT applications, the intercepted data could be a patient's personal or healthcare information, which presents a severe ethical problem.”
hedgedstates a cause- Asserted: passes — The authors put it forward as true.
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In the paper
“It provides a highly trained ML model without the risk of exposing training data since they are kept where they are generated.”
What the sources say
- Not checked yetcites [7] source
plainstates a cause- Asserted: passes — The authors put it forward as true.
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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
“It is an efficient solution to address these issues and reduce the network congestion and latency that occur with the cloud.”
plainstates a cause- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
In the paper
“The experimental results on human activity recognition demonstrate the effectiveness of the FedHome framework in terms of accuracy, computation, and communication overhead.”
What the sources say
- Not checked yetcites [46] source
boosted- Asserted: passes — The authors put it forward as true.
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In the paper
“As a result, the recent technique of transfer learning, which avoids learning from scratch and solves the problem of insufficient training data, can be effective in this scenario [42].”
What the sources say
- Not checked yetcites [42] source
plainstates a cause- Asserted: passes — The authors put it forward as true.
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In the paper
“The theoretical and experimental comparisons show that the proposed model converges faster and with less communication overhead compared to an alternative distributed algorithm.”
What the sources say
- Not checked yetcites [35] source
plainstates a cause- Asserted: passes — The authors put it forward as true.
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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
“Some clients, for example, may deliberately try to extract information from other parties, degrade performance, or bring the system down.”
hedgedstates a possible cause- Asserted: passes — The authors put it forward as true.
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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
“While standards are necessary for security and privacy purposes, they can make sharing and using health data challenging.”
plainstates a possible cause- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no 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
“However, operating FL systems on a larger scale might be impractical to establish collaborative agreements and it is more reasonable not to have trustworthiness assumptions.”
hedgedstates a possible cause- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. 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
“For FL consortia in which all parties are bounded by an enforceable collaboration agreement, we can disregard some of the more nefarious motivations, such as deliberated attempts to extract sensitive information or to intentionally corrupt the model.”
plainstates a cause- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
“In view of the above arguments, precious data often remain confined to individual institutions and are rarely leveraged for analysis, hindering the application of deep learning algorithms in healthcare.”
plainstates a cause- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no 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
“Besides, the model can be manipulated by attackers inducing additional memorization through gradient-ascent-style attacks.”
plainstates a cause- Asserted: passes — The authors put it forward as true.
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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
“In fact, personal health information is considered to be highly sensitive, containing not only diagnostic and healthcare related information but also identifiable details about individuals.”
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.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“Bonawitz et al. [62] introduce a secure aggregation protocol for protecting individual model updates.”
What the sources say
- Not checked yetcites [62] source
plainstates a cause- 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.
In the paper
“The applied secure aggregation is a lossless method that can retain the original accuracy with a very high privacy guarantee.”
What the sources say
- Not checked yetcites [62] source
plainstates a cause- 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.
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
“At the same time, it can be noted how some sophisticated countermeasures can be avoided if all parties are deemed trustworthy.”
plainstates a cause- 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.
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
“Lastly, privacy problems are exacerbated in medical companies by strict laws and regulations designed to prevent the risk of re-identification and data breaches.”
plainstates a cause- 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
“If these standards are respected, FL can foster the application of deep learning in digital health and healthcare informatics, making it possible the use of existing health datasets while protecting user privacy.”
plainstates a possible cause- 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: 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 may give no 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
“Even from the consumer perspective, data privacy is one of the public's most important concerns because data breaches can result in reduced public trust.”
plainstates a cause- 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: 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’s conclusions may not depend on it. It may state two separate claims as one.
- Rests on an assumption: Data breaches can result in reduced public trust.
In the paper
“In this solution, the central server is not able to see any local updates, but it can still observe the exact aggregated results at each round.”
What the sources say
- Not checked yetcites [62] source
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.
In the paper
“Li et al. [65] introduced locally differentially private algorithms in the context of meta-learning while providing provable learning guarantees in convex settings.”
What the sources say
- Not checked yetcites [65] Li, 2019, Differentially private meta-learning
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. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Another approach to establish data security and eliminate the trust issues is the deployment of FL on blockchain technology [71]- [73].”
What the sources say
plainstates a cause- 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.
In the paper
“This type of approach comes with a considerable number of hyperparameters that need to be carefully selected and that affect communication and accuracy.”
What the sources say
- Not checked yetcites [63] McMahan, 2017, Learning differentially private recurrent language models
plainstates a cause- 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.
In the paper
“The simulation results show that PRCL reduces the local training overhead because outsourcing the middle part to the cloud server while providing similar accuracy to other state-of-the-art approaches.”
What the sources say
- Not checked yetcites [68] source
plainstates a cause- 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.
In the paper
“If lower the respective target values, the lower the trust value of the client, and when such a trust value is lower than 60%, the request from this client will be prohibited.”
What the sources say
- Not checked yetcites [70] source
plainstates a cause- 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.
In the paper
“For stronger privacy guarantees, Bhowmick et al. [64] have proposed a relaxed version of local privacy by limiting the power of potential adversaries.”
What the sources say
- Not checked yetcites [64] Bhowmick, 2018, Protection against reconstruction and its applications in private federated learning
plainstates a cause- 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.
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
“In addition, the blockchain-based verification scheme can also improve the reliability of the federated training process.”
plainstates a cause- 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.
None of the sources cited for this claim have been checked yet.
In the paper
“Adopting the homomorphisms of secret sharing, it preserves user privacy and requires less computing and communication resources than alternatives as [62], and is able to provide acceptable fault tolerance in the event of user disconnection.”
plainstates a cause- 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.
In the paper
“At the same time, confidentiality is preserved since the cloud cannot access the plaintext gradient and an attacker can only get the perturbed data due to the added noise.”
What the sources say
- Not checked yetcites [68] source
plainstates a cause- 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
“Providing stronger guarantees than global privacy, it has better model performance than strict local privacy.”
What the sources say
- Not checked yetcites [64] Bhowmick, 2018, Protection against reconstruction and its applications in private federated learning
plainstates a cause- 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.
In the paper
“Similar results have been achieved by EPPDA [69], an efficient privacy-preserving data aggregation method for FL that, based on secret sharing to resist the reverse attack, can covertly aggregate user-trained models without disclosing the user model.”
What the sources say
- Not checked yetcites [69] source
plainstates a cause- 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.
In the paper
“Wang et al.proposed a defensive strategy based on comparing the uploaded model's accuracy and size [70].”
What the sources say
- Not checked yetcites [70] source
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.
In the paper
“Lu et al. [77] designed a new system, called DITEN, that integrates blockchain and FL in edge networks and uses Deep Neural Networks (DNN) as a strategy scheduler to ensure the privacy of user data and enhance learning security.”
What the sources say
- Not checked yetcites [77] source
plainstates a cause- 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.
In the paper
“In such a way, block generation efficiency is guaranteed, enabling decentralized privacy protection while preventing single point failure.”
What the sources say
- Not checked yetcites [72] source
plainstates a cause- 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.
In the paper
“Such a solution offers several advantages such as decentralization, non-tempering, open autonomy, and anonymous traceability [75].”
What the sources say
- Not checked yetcites [75] source
plainstates a cause- 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.
In the paper
“To empower FL with this approach, Qu et al. [72] propose to store only the pointer of the global updates on-chain, while a distributed hash table is used to save the data.”
What the sources say
- Not checked yetcites [72] source
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. 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
“A blockchain-based FL, however, should mitigate the overhead of frequent communications in the blockchain consensus, which can cause excessive latency.”
plainstates a cause- 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.
In the paper
“The solution can also identify and eliminate outliers in the training distributions before combining the model updates.”
What the sources say
- Not checked yetcites [76] source
plainstates a cause- 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
“Two key aspects must be considered for an effective reduction of the communication overhead: the total number of communication rounds, and the size of messages transmitted in each round.”
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. It may state two separate claims as one.
In the paper
“The most commonly used method for FL, FedAvg [29], is based on averaging local stochastic gradient descent (SGD) updates for the primal problem.”
What the sources say
- Not checked yetcites [29] McMahan, 2017, Communication-efficient learning of deep networks from decentralized data
plainstates a cause- 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
“However, in practice, these solutions suffer from several issues and challenges that should be considered when designing FL-based approaches for healthcare services.”
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.
None of the sources cited for this claim have been checked yet.
In the paper
“Some recent techniques can partially solve these privacy issues [61], however, the more secure the techniques, the lower the accuracy of the final model may be [80].”
hedgedstates a possible cause- 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.
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
“We can mention two main obstacles: (i) a high volume of aggregated data from different parties to be processed by the server; (ii) none of these parties fully trust each others (including the server), thus fearing data leakage.”
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.
In the paper
“Although these asynchronous methods are inherently robust to stragglers, they may suffer from slow convergence due to the use of stale models.”
What the sources say
- Not checked yetcites [82] Dutta, 2018, Slow and stale gradients can win the race: Error-runtime trade-offs in distributed SGD
hedgedstates a possible cause- 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.
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
“Moreover, decentralizing can be combined with blockchain technology, where a blockchain-based approach can jointly solve the problem of single-point-of-failure and central trust.”
plainstates a cause- 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.
In the paper
“Federated networks of IoMT can indeed comprise a massive number of devices (e.g., sensors), and communication in the network can be many orders of magnitude slower than local computation due to limited resources, such as bandwidth, energy, and power [86].”
What the sources say
- Not checked yetcites [86] source
plainstates a cause- 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
“While FedAvg has been shown to work well for non-convex problems, it does not exhibit convergence guarantees and can diverge in practical settings when data are heterogeneous [32] (see Section V-D for how to address this problem).”
What the sources say
- Not checked yetcites [32] Li, 2020, Federated optimization in heterogeneous networks
plainstates a cause- 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.
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
“With either solution, i.e., peer-to-peer or blockchain, the problem is converted to the pursuit of fault tolerance for the clients.”
plainstates a cause- 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.
In the paper
“Other recent methods aiming to enhance the privacy of federated learning use tools such as secure multiparty computation or differential privacy.These approaches often provide privacy at the cost of reduced model performance or system efficiency [62].”
What the sources say
- Not checked yetcites [62] source
plainstates a cause- 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.
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
“In conclusion, the features of FL make it uniquely suited for sensitive data as in the context of IoMT systems, but the architecture and model sharing must address further challenges compared to other fields.”
plainstates a cause- 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. 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
“However, while this distributed approach can help to make the system highly scalable, it also opens up some issues in gradient synchronization.”
plainstates a cause- 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
“We can mention two main obstacles: (i) a high volume of aggregated data from different parties to be processed by the server; (ii) none of these parties fully trust each others (including the server), thus fearing data leakage.”
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.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“While blockchain removes the need for a trusted curator and connects each participant through multiparty data retrieval, it also strengthens the data-sharing scheme and provides some fault-tolerant guarantees [83].”
What the sources say
- Not checked yetcites [83] source
plainstates a cause- 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.
In the paper
“In a similar way, Lu et al. [78] integrated federated learning into the consensus algorithm of the blockchain to save the hash rate in the context of industrial IoT, in which blockchain enables secure data retrieval and ensures accurate model training.”
What the sources say
- Not checked yetcites [78] source
plainstates a cause- 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.
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
“Lastly, decentralized training is a potential alternative in FL to reduce high communication costs on the central server.”
hedgedstates a possible cause- 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.
In the paper
“To further improve the transmission of model output, the approach is combined with Federated Augmentation (FAug), a data augmentation scheme that can collectively learn the trade-off between privacy leakage and communication overhead using a Generative Adversarial Network (GAN).”
What the sources say
- Not checked yetcites [91] Jeong, 2018, Communication-efficient on-device machine learning: Federated distillation and augmentation under non-IID private data
plainstates a cause- 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.
In the paper
“Moreover, Jeong et al. [91] presented Federated Distillation (FD), a distributed model training algorithm whose communication payload size is much smaller than that of a benchmark FL scheme, particularly when the model size is large.”
What the sources say
- Not checked yetcites [91] Jeong, 2018, Communication-efficient on-device machine learning: Federated distillation and augmentation under non-IID private data
plainstates a cause- 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.
In the paper
“FedProx [32], for instance, tolerates that any selected device to perform partial work, in compliance with the underlying system's constraints, and can safely incorporate these partial updates via a proximal term.”
What the sources say
- Not checked yetcites [32] Li, 2020, Federated optimization in heterogeneous networks
plainstates a cause- 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.
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
“However, for medical data collected by IoMT simple models can be likely inaccurate.”
hedgedstates a possible cause- 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.
In the paper
“On top of conventional periodic communication of FL, the proposed FD exchanges, not the model parameters but the model output, so that large local models can be adopted on the ML device.”
What the sources say
- Not checked yetcites [91] Jeong, 2018, Communication-efficient on-device machine learning: Federated distillation and augmentation under non-IID private data
plainstates a cause- 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.
In the paper
“In addition, while a large number of clients can increase the diversity of the local model across them, this may degrade the performance of FL [93].”
What the sources say
- Not checked yetcites [93] Zhang, 2020, Benchmarking semisupervised federated learning
hedgedstates a possible cause- 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.
In the paper
“By selecting the devices with favorable resources, FedCS provides high classification accuracy in a significantly shorter time compared to the classical FL.”
What the sources say
- Not checked yetcites [94] source
plainstates a cause- 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.
In the paper
“Although some recent articles have investigated decentralized training over heterogeneous data with local updating schemes, e.g., [90], they are either restricted to linear models or assume full device participation.”
What the sources say
- Not checked yetcites [90] He, 2018, Cola: Decentralized linear learning
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.
In the paper
“Results confirm that using less complex models, such as softmax regression, is a viable solution for most real-world applications since user behavior phenomena are often simple enough to be captured by relatively simple models.”
What the sources say
- Not checked yetcites [37] source
boostedstates a cause- 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.
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
“Although a high number of local updating methods can effectively reduce the total number of communication rounds, it can be convenient to reduce the size of messages exchanged at each round via compression schemes model, e.g., sparsification, subsampling, and quantization.”
plainstates a cause- 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.
In the paper
“The authors showed how using CNNs and LSTMs, the communication costs can be reduced by two orders of magnitude compared to the original federated learning algorithm.”
What the sources say
- Not checked yetcites [88] Konen, 2016, Federated learning: Strategies for improving communication efficiency
plainstates a cause- 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
“IoMT devices from remote areas may be proner to drop due to poor network connectivity or battery failure and, therefore, the trained federated model will be biased towards devices with favorable network conditions.”
hedgedstates a possible cause- 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.
In the paper
“Recent studies present a jointannouncement protocol that can randomly select some devices/ clients from a large number of participants in a given training round [85].Such a joint-announcement protocol is useful to alleviate the out-of-sync issue and tolerate the dropout of some clients at some points before the completion of each training iteration.”
What the sources say
- Not checked yetcites [85] Bonawitz, 2019, Toward federated learning at scale: System design
plainstates a cause- 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.
None of the sources cited for this claim have been checked yet.
In the paper
“However, while several recent studies have investigated convergence guarantees of variants of FL methods [95]- [97], little work has evaluated the impact of failures over the FL algorithm or studied directly the effect of dropped devices.”
What the sources say
- Not checked yetcites [95] Jiang, 2018, A linear speedup analysis of distributed deep learning with sparse and quantized communication
- Not checked yetcites [96] source
- Not checked yetcites [97] Yu, 2019, On the linear speedup analysis of communication efficient momentum SGD for distributed non-convex optimization
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.
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
“Both approaches (multi-task and meta-learning) enable personalized or device-specific modeling, which is considered a valuable attempt to handle the statistical heterogeneity of the data.”
plainstates a cause- 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.
In the paper
“Once the trained generator of the GAN is downloaded, each device can replenish the target labels until an IID training dataset is achieved”
What the sources say
- Not checked yetcites [91] Jeong, 2018, Communication-efficient on-device machine learning: Federated distillation and augmentation under non-IID private data
plainstates a cause- 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
“This paradigm of data generation violates the frequently-used assumptions of independent and identically distributed (IID) data and may cause problems in modeling, theoretical analysis, and empirical evaluation of solutions.”
hedgedstates a possible cause- 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
“Therefore, new schemes ensuring fault tolerance would improve the deployment of FL in healthcare where IoMT devices are likely to a fault, encounter challenging network conditions, and exhaust their batteries.”
hedgedstates a cause- 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: 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’s conclusions may not depend on it.
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
“In addition to the variety of data, the number of data samples can vary significantly from device to device, and there may be an underlying statistical structure that captures the relationship among devices and their associated distributions.”
hedgedstates an association- 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.
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
“However, despite introducing a trade-off between accuracy and centralization, this could lead to significant communication overhead, especially for medical data.”
hedgedstates a cause- 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.
In the paper
“To rectify the non-IID training dataset while addressing the communication overhead, Jeong et al. [91] proposed FAug, in which each device can generate the missing data samples locally using a generative model, i.e., a conditional GAN.”
What the sources say
- Not checked yetcites [91] Jeong, 2018, Communication-efficient on-device machine learning: Federated distillation and augmentation under non-IID private data
plainstates a cause- 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.
In the paper
“It addresses the statistical challenge of FL when local data is non-IID by distributing a small amount of globally shared data containing examples from each class.”
What the sources say
- Not checked yetcites [31] Zhao, 2018, Federated learning with non-IID data
plainstates a cause- 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.
In the paper
“With ELFISH, the neurons that have less significant weight parameter updates will be randomly masked.”
What the sources say
- Not checked yetcites [107] Xu, 2019, ELFISH: Resource-aware federated learning on heterogeneous edge devices, Ratio
plainstates a cause- 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.
In the paper
“Furthermore, Anh et al. [109] applied a Deep Q-learning algorithm based on the Double Deep Q-Network (DDQN) to optimize resource allocation for model training.”
What the sources say
- Not checked yetcites [109] source
plainstates a cause- 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.
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
“For example, training CNN-based models for COVID-19 detection (e.g., ResNet-50) requires storage and memory resources that may not be available on all devices.”
hedgedstates a possible cause- 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.
In the paper
“The simulation results show that the proposed approach can minimize energy consumption and improve the training latency.”
What the sources say
- Not checked yetcites [109] source
plainstates a cause- 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.
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
“While machine learning approaches, and especially deep learning, are becoming the de-facto knowledge discovery approach in digital healthcare, it appears that data-driven medicine aiming to improve patient care globally requires federated efforts.”
hedgedstates a cause- 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: 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 may give no reason why it holds.
- Rests on an assumption: Data-driven medical methods face scarcity and privacy of user data, as well as difficulty of institutions exchanging medical information.
- Rests on an assumption: With insufficient data, ML is prevented from reaching its full potential, which is only possible if the database consists of the full spectrum of possible anatomies, pathologies, and input data types.
In the paper
“Similarly, Xu et al. [107] proposed a resource-aware federated learning framework, called ELFISH.”
What the sources say
- Not checked yetcites [107] Xu, 2019, ELFISH: Resource-aware federated learning on heterogeneous edge devices, Ratio
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.
In the paper
“In the same direction, Jiang et al. [108] proposed a new FL paradigm called PruneFL in order to minimize the model's size.”
What the sources say
- Not checked yetcites [108] source
plainstates a cause- 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.
In the paper
“Simulation results demonstrate the superior performance of this solution by reducing the local computations by 1.7× without affecting the accuracy of the model.”
What the sources say
- Not checked yetcites [89] Caldas, 2018, Expanding the reach of federated learning by reducing client resource requirements
boostedstates a cause- 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.
None of the sources cited for this claim have been checked yet.
In the paper
“In addition, data pre-processing is an important step to remove the redundancy in the training set as well as to find the relevant features to be used during the model training [110], [111].”
plainstates a cause- 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.
In the paper
“A possible solution in which edge computing provides complementary resources in support of the local model training [105], would require time-consuming data transmission between the nano-devices and the edge nodes and could lead to network congestion, especially given the huge amount of data generated by these devices every second.”
What the sources say
- Not checked yetcites [105] source
hedgedstates a cause- 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
“Caldas et al. [89] introduce Federated Dropout to reduce the computation load of local training.”
What the sources say
- Not checked yetcites [89] Caldas, 2018, Expanding the reach of federated learning by reducing client resource requirements
plainstates a cause- 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.
In the paper
“The experimental results show that PruneFL always converges to similar accuracy achieved by classical FL.”
What the sources say
- Not checked yetcites [108] source
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
“Already today, FL is improving medical image analysis by providing clinicians with better diagnostic tools, helping find similar patients for true precision medicine, and optimizing the drug discovery process by reducing costs and time-to-market for pharmaceutical companies.”
plainstates a cause- 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
“Already today, FL is improving medical image analysis by providing clinicians with better diagnostic tools, helping find similar patients for true precision medicine, and optimizing the drug discovery process by reducing costs and time-to-market for pharmaceutical companies.”
plainstates a cause- 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.
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
“However, FL is still unable to deliver all its promises and meets the more stringent requirements (e.g., latency, security) of a healthcare system based on multiple Internet of Medical Things (IoMT).”
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.
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
“Already today, FL is improving medical image analysis by providing clinicians with better diagnostic tools, helping find similar patients for true precision medicine, and optimizing the drug discovery process by reducing costs and time-to-market for pharmaceutical companies.”
plainstates a cause- 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.
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
“However, the current settings of FL have not answered all the technical questions”
plain- 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.
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
“For example, although no data are shared among the participants by definition in FL systems, some security risks are still present and can be considered as vulnerabilities from multiple aspects.”
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.
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
“To solve these issues, Federated Learning (FL) appeared as a valuable approach in the medical field, allowing patient data to stay where it is generated.”
hedgedstates a cause- 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.
- Rests on an assumption: Data-driven medical methods face scarcity and privacy of user data, as well as difficulty of institutions exchanging medical information.
- Rests on an assumption: With insufficient data, ML is prevented from reaching its full potential, which is only possible if the database consists of the full spectrum of possible anatomies, pathologies, and input data types.
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
“With the promise of powerful, accurate, safe, robust, and unbiased models, federated learning can neatly protect sensitive medical data while collaboratively training a learning model.”
- Also stated in: § VI. CONCLUSION · plain
“Since an FL setting allows many clients to collaboratively train a model while keeping training data decentralized, it can protect privacy-sensitive medical data.”
plainstates a cause- 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.
- Also stated in: § VI. CONCLUSION · plain
The paper relies on this without support.
Medical data is highly sensitive and must be protected by appropriate confidentiality procedures.
“Medical data is highly sensitive and must be protected by means of appropriate confidentiality procedures.”
The paper relies on this without support.
Security problems in FL are primarily caused by curious or malicious attackers targeting vulnerabilities of the FL system, which can lead to significant performance drop and sometimes model invalidation.
“This type of problem is primarily caused by curious or malicious attackers targeting vulnerabilities of the FL system, which can lead to significant performance drop and sometimes model invalidation.”
The paper relies on this without support.
In healthcare and IoMT scenarios, an attacker can directly manipulate the model and data of a local affiliation, resulting in a malicious update of the global model or mislabeled data.
“In the context of healthcare and IoMT scenarios, an attacker can directly manipulate the model and data of a local affiliation, resulting in a malicious update of the global model or mislabeled data.”
The paper relies on this without support.
Increased heterogeneity of data sources can decrease model performance, bottleneck the network, and increase storage and processing costs.
“Moreover, the increase in heterogeneity of data sources could decrease the performance of the model, bottleneck the whole network, and cause an extra computational cost for both storage and processing.”
The authors state this as an assumption.
In global privacy-enhancing mechanisms, the global server is trusted.
“In other words, in a global privacy-enhancing mechanism, the global server is assumed trusted, while in local privacy, the central server may be malicious.”
The paper relies on this without support.
Data breaches can result in reduced public trust.
“because data breaches can result in reduced public trust.”
The paper relies on this without support.
Some IoMT devices, even though not limited in size, have limitations in the available on-board resources.
“Some IoMT devices, even though not limited in size, come with limitations in the available on-board resources.”
The paper relies on this without support.
Data-driven medical methods face scarcity and privacy of user data, as well as difficulty of institutions exchanging medical information.
“Despite the profitable usage of AI-based algorithms, these data-driven methods are facing issues such as the scarcity and privacy of user data, as well as the difficulty of institutions exchanging medical information.”
The paper relies on this without support.
With insufficient data, ML is prevented from reaching its full potential, which is only possible if the database consists of the full spectrum of possible anatomies, pathologies, and input data types.
“With insufficient data, ML is prevented from reaching its full potential, which is only possible if the database consists of the full spectrum of possible anatomies, pathologies, and input data types.”
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?
In the citing paper
“However, some of them focus on electronic health records [21,22] or internet of medical things [26], without paying attention to medical imaging.”
From Federated learning for medical image analysis: A survey (2024) · cites this paper as [26]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ A. Federated Transfer Learning
“Chan et al. [43] proposed a Federated Transfer Learning (FTL) framework for remote healthcare monitoring, called FedHealth”
§ References
“43. Y. Chen, X. Qin, J. Wang, C. Yu, and W. Gao, "FedHealth: A federated transfer learning framework for wearable healthcare," IEEE Intell. Syst., vol. 35, no. 4, pp. 83-93, Jul./Aug. 2020.”
Citations
Cites
(2014) · Cited by 33,419
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
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 · cited 2×
“differential privacy [60], the study of effective methods to improve the security and privacy protection is still an active area of research [61].”
Deep 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
Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2975749 · Cited by 6,210
Federated Optimization in Heterogeneous Networks
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2018) · arXiv · doi:10.48550/arxiv.1812.06127 · Cited by 4,023
The future of digital health with federated learning
Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00323-1 · Cited by 4,015 · cited 3×
“It also improves patient privacy and reduces low latency during the collaboration of multiple hospitals/entities [7].”
InfluentialPractical Secure Aggregation for Privacy-Preserving Machine Learning
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3133982 · Cited by 3,956
Federated Learning: Strategies for Improving Communication Efficiency
Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016) · arXiv · doi:10.48550/arxiv.1610.05492 · Cited by 3,766
Artificial intelligence and statistics
Yu, B., & Kumbier, K. (2018) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1700813 · Cited by 2,367
Research works printed but not linked: 1
- Bonawitz, 2019, Toward federated learning at scale: System design, 1, 374
Cited by
Sorted by citations
Federated learning for medical image analysis: A survey
Guan, H., Yap, P.-T., Bozoki, A., & Liu, M. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110424 · Cited by 518
A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions
Aouedi, O., Vu, T.-H., Sacco, A., Nguyen, D. C., Piamrat, K., Marchetto, G., & Pham, Q. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3430368 · Cited by 345
Self-citationPrivacy preservation for federated learning in health care
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Metadata
- Authors
- Aouedi, O., Sacco, A., Piamrat, K., & Marchetto, G.
- Affiliations
- Laboratoire des Sciences du Numérique de Nantes (FR)
- Politecnico di Torino (IT)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2023-02
- Volume
- 27
- Issue
- 2
- Pages
- 790-803
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W4283373300
- Semantic Scholar
- c1fa2708bc5fe04825485d5f93c4bf7e45d1d1fd
- Cited by
- 165
- References
- 111
- Citations by year
- 2026: 44 · 2025: 60 · 2024: 44 · 2023: 15
- FWCI
- 9.95
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PORTO@Iris (submittedVersion, cc-by)
- HAL (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Keywords
- federated learning, privacy-preserving machine learning, medical data privacy, Internet of Medical Things, security vulnerabilities, data scarcity, health data sharing
- MeSH terms
- Internet of Things, Algorithms, Humans, Databases, Factual, Privacy, Genomics
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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