The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index; 1 of the 28 sources its claims cite were checked for support.
Federated learning meets blockchain in decentralized data-sharing: Healthcare use case
Open accessAbstract
In the era of data-driven healthcare, the amalgamation of blockchain and Federated Learning (FL) introduces a paradigm shift towards secure, collaborative, and patient-centric data-sharing. This paper pioneers the exploration of the conceptual framework and technical synergy of FL and blockchain for decentralized data-sharing, aiming to strike a balance between data utility and privacy. FL, a decentralized machine learning paradigm, enables collaborative AI model training across multiple healthcare institutions without sharing raw patient data. Combined with blockchain, a transparent and immutable ledger, it establishes an ecosystem fostering trust, security, and data integrity. The paper elucidates the technical foundations of FL and blockchain, unravelling their roles in reshaping healthcare data-sharing. The paper vividly illustrates the potential impact of this fusion on patient care. The proposed approach preserves patient privacy while granting healthcare providers and researchers access to diversified datasets, ultimately leading to more accurate models and improved diagnoses. The findings underscore the potential acceleration of medical research, improved treatment outcomes, and patient empowerment through data ownership. The synergy of FL and blockchain envisions a healthcare ecosystem that prioritizes individual privacy and propels advancements in medical science.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The paper proposes a conceptual framework combining federated learning and blockchain for decentralized healthcare data-sharing, using FL to keep raw data local and blockchain for encrypted, immutable transaction records. It illustrates the approach with the Iris dataset and simulated attacks, reporting low attack success rates but no real-world healthcare deployment.
Key findings
- The FL–blockchain synergy is claimed to enhance security, privacy, interoperability, transparency, and resilience for decentralized data-sharing, addressing the trade-off between data utility and patient privacy.
- The framework is demonstrated with the Iris dataset: 150 samples from three Iris species, with four features; FL keeps raw data local, while encrypted attributes (sepal length, sepal width, petal length, petal width) are recorded on the blockchain and the species label remains unencrypted metadata.
- Simulated adversarial Hospital C launched replay, identity masquerade, and man-in-the-middle attacks; defense mechanisms—nonce/hash verification, RSA-based digital signatures with IP verification, and end-to-end encryption—produced low success rates across attack types, with man-in-the-middle slightly higher, as shown in Fig. 8 (no numeric values are given in the text).
- Healthcare opportunities identified include remote patient monitoring, collaborative research and treatment optimization, and regulatory compliance/consent management for HIPAA, GDPR, and similar rules.
Methods
- Conceptual framework and architecture integrating FL and blockchain, with algorithms for data retrieval, data transaction, proof-of-work, authorization checks, and adversarial attack simulation.
- Use case involving hospitals A and B with local datasets; the Iris dataset serves as the primary dataset, FL handles preprocessing/distribution, blockchain stores encrypted attributes, and Hospital C acts as a malicious third party.
- Evaluation is qualitative/empirical: attack success rates are presented in Fig. 8, and complexity analysis is given for encryption, retrieval \(O(n*d)\), proof-of-work \(O(2^k)\), and authorization \(O(n)\) or \(O(\log n)/O(1)\).
Limitations
- The paper states challenges including interoperability, scalability, consensus, security, privacy, and data quality; the conclusion also states that “a specific real-world healthcare use case is not presented.”
- Evident from the text: the evaluation uses the Iris dataset rather than real healthcare data, and no FL model accuracy, clinical outcomes, or deployment results are reported.
- Evident from the text: attack success rates are described only qualitatively from Fig. 8 without numeric values, and scalability, energy consumption, and regulatory compliance are discussed as challenges rather than empirically tested.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
47 Own interpretation · 22 Not checked yet · 13 No basis given · 3 Own method · 3 Own result · 1 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
“Through these defense mechanisms, our decentralized datasharing blockchain system demonstrated resilience against the common threats posed by adversarial entities.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > G. Defense Mechanisms
boosteddirect- 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.
- Rests on an assumption: The simulated attacks represent common threats to the system.
- Rests on an assumption: Public-key cryptography is secure for encrypting data attributes.
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
“Replay attacks get detected as the blockchain verifies the nonce and hash values, and a reused nonce value indicates a replay attempt.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > G. Defense Mechanisms
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Public-key cryptography is secure for encrypting data attributes.
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
“Against the backdrop of these simulated attacks, our blockchain implementation showcased several defense mechanisms:”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > G. Defense Mechanisms
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The simulated attacks represent common threats to the system.
- Rests on an assumption: Public-key cryptography is secure for encrypting data attributes.
In the paper
“FL incorporates privacy-enhancing techniques like differential privacy, introducing statistical noise into data or model updates, rendering re-identifying individuals based on shared information exceedingly tricky.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > C. Federated Learning
What the sources say
- Not checked yetcites [58] Bang, Design of personal data protection decentralized model using blockchain and IPFS
plaindirect- 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
“FL, a decentralized machine learning paradigm, enables collaborative AI model training across multiple healthcare institutions without sharing raw patient data.”
- Also stated in: § I. INTRODUCTION · plain
“FL, a decentralized Machine Learning (ML) approach that Google [12] pioneered offers a novel paradigm for collaborative model training across a network of data sources without centralizing raw data.”
- Also stated in: § B. Related work · plain
“FL allows multiple parties to train an ML model collaboratively without sharing raw data.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > Abstract
What the sources say
- Not checked yetcites [12] McMahan, Communication-efficient learning of deep networks from decentralized data
plaindirect- Asserted: passes — The authors put it forward as true.
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- Also stated in: § I. INTRODUCTION · plain
In the paper
“It inherently safeguards data privacy at its source, a crucial factor in healthcare, where data confidentiality is sacrosanct [13].”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > I. INTRODUCTION
What the sources say
- Not checked yetcites [13] source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“These updates do not reveal the raw data from which they were derived, further fortifying data privacy [57].”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > C. Federated Learning
What the sources say
- Not checked yetcites [57] source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Each node possesses a private key [46], ensuring only intended recipients can access shared data, even if the network is compromised.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > A. Decentralized data-sharing
What the sources say
- Not checked yetcites [46] source
plaindirect- 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
“Primarily, it fortifies data security through its distributed structure, rendering it resistant to targeted cyber-attacks or data breaches [43] [44].”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > A. Decentralized data-sharing
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Additionally, FL is designed to handle device failures and data corruption robustly.Should a device go offline or experience data corruption, the FL process continues with minimal disruption, as it relies on numerous other devices that persist in their local computations.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > C. Federated Learning
What the sources say
- Not checked yetcites [61] source
plaindirect- Asserted: passes — The authors put it forward as true.
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- It may state two separate claims as one. 42
- Probably not a substantial claim. 49
Every source the paper cites for this claim was checked and supports it.
In the paper
“FL presents an innovative approach to ML that prioritizes collaborative model training while preserving data privacy and security [55].”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > C. Federated Learning
What the sources say
- Supportedcites [55] source
The Introduction (p. 1) describes clients collaboratively updating a model through server aggregation while keeping their data secure in local storage. The Federated Learning section (p. 2) likewise says clients do not upload private data, supporting the privacy and security characterization. (LLM, conf=0.92)
plaindirect- Asserted: passes — The authors put it forward as true.
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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
“Proof-of-work is inherently designed to be computationally intensive.The complexity is not fixed and is adjusted by the difficulty target D. The average time complexity of finding a valid nonce is proportional to the difficulty target, which is typically exponential concerning the number of leading zeros required in the hash output.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > D. Consensus Mechanism: Proof-of-Work
plaincorrelational- 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
“If n represents the number of transactions and d the decryption time, the total time complexity would be O(n*d), assuming the filter operation's complexity is less than or equal to O(n).”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > 3) Data Retrieval and Analysis:
hedgeddirect- 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
“The data transaction process involves encryption and signing operations.Both operations are considered polynomial time complexity based on the key sizes used for encryption and signing.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > C. Data Transaction
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. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: Public-key cryptography is secure for encrypting data attributes.
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
“If the registry is unsorted and has n entries, this operation has a worst-case time complexity of O(n).If the registry is sorted or hashed, the time complexity could be reduced to O(log n) or even O(1), respectively.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > Algorithm 4 Authorization Check
hedgeddirect- 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.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The graph in Fig. 8 shows a low success rate for replay attacks, remaining consistently low across multiple attempts.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > H. Evaluation
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The complexity of adding a new block involves calculating the hash and performing the proof-of-work, which has a complexity of O(2 k ) on average, where k is the number of bits required by the difficulty target D.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > 3) Data Retrieval and Analysis:
plaindirect- 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
“The digital signature verification will fail if Hospital C tries to masquerade as Hospital A or B. Additionally, IP address checks were implemented to add an extra layer of verification, further thwarting identity masquerade attempts.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > G. Defense Mechanisms
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Rests on an assumption: Public-key cryptography is secure for encrypting data attributes.
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
“The graph in Fig. 8 suggests that man-in-the-middle attacks have a slightly higher success rate than the other two types but remain relatively low.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > H. Evaluation
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The transmission complexity depends on network factors and is typically considered O(1) in the context of algorithmic analysis.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > C. Data Transaction
plain- Asserted: passes — The authors put it forward as true.
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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
“The graph in Fig. 8 corroborates this: the success rate for identity masquerade attacks is also low and does not show a significant increase with more attempts.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > H. Evaluation
plain- 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
“This ensures that even if Hospital C intercepts the data in a man-in-the-middle attack, it cannot decrypt or modify it without the corresponding private key.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > G. Defense Mechanisms
plaindirect- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: Public-key cryptography is secure for encrypting data attributes.
In the paper
“FL significantly augments data security and privacy by retaining sensitive information locally, thereby reducing the risk of data breaches during transmission [56].”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > C. Federated Learning
What the sources say
- Not checked yetcites [56] source
boosteddirect- 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
“Several decentralized data-sharing technologies and techniques, such as Federated Learning (FL) [10] and blockchain [11], have emerged as promising solutions to address these challenges.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > I. INTRODUCTION
plainconditional- 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
“Combining both mitigates risks associated with individual participant failures.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > C. Federated Learning
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
- Rests on an assumption: Blockchain provides a secure, transparent ledger for recording transactions and maintaining data integrity.
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 creates an environment of trust and accountability among players by protecting intellectual property rights and promoting openness.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > A. Motivation and Contributions
plaindirect- 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. 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
“This paper represents a pioneering effort to integrate these technologies specifically for decentralized healthcare data-sharing.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > B. Related work
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
“This approach leads to more accurate medical models, personalized treatment options, and improved patient care, revolutionizing healthcare.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > A. Motivation and Contributions
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“datasharing enhances transparency and accountability, acting as a bulwark against corruption and building trust among stakeholders [18].”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > A. Motivation and Contributions
What the sources say
- Not checked yetcites [18] source
plaindirect- 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
“Using blockchain in conjunction with FL can provide increased security and privacy, while also ensuring a transparent and fair training process.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > C. Federated Learning
plaindirect- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: Blockchain provides a secure, transparent ledger for recording transactions and maintaining data integrity.
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
“We propose an innovative framework that enables healthcare institutions, researchers, and patients to share data securely and efficiently.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > A. Motivation and Contributions
plaindirect- 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
“Combining both ensures interoperable data-sharing mechanisms and a common data usage framework.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > C. Federated Learning
plaindirect- 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: Blockchain provides a secure, transparent ledger for recording transactions and maintaining data integrity.
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
“The combination encourages active data contribution.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > C. Federated Learning
plaindirect- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: Blockchain provides a secure, transparent ledger for recording transactions and maintaining data integrity.
In the paper
“Furthermore, consensus algorithms verify data accuracy [47], fortifying security and control over data access.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > A. Decentralized data-sharing
What the sources say
- Not checked yetcites [47] source
plaindirect- 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
“This integration is poised to revolutionize the healthcare landscape, fostering advancements in research, improving patient care, and ensuring regulatory compliance, all while maintaining a steadfast focus on patient privacy.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > A. Motivation and Contributions
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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
“The synergy of blockchain and FL strategy guarantees enhanced security, privacy and a strong barrier against unwanted access and possible data breaches.”
- Also stated in: § A. Motivation and Contributions · hedged
“This method promises enhanced security and privacy, mitigating risks like unauthorized access and data breaches.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > A. Motivation and Contributions
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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.
- Also stated in: § A. Motivation and Contributions · hedged
None of the sources cited for this claim have been checked yet.
In the paper
“However, centralized data-sharing systems have limitations, such as data privacy and security concerns [5]interoperability issues [6], and single points of failure [7].”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > I. INTRODUCTION
What the sources say
plaincorrelational- 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
“By amalgamating FL's capability to train models without exposing raw data and blockchain's strength in maintaining data integrity, we ensure patient information's confidentiality and immutability.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > A. Motivation and Contributions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
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
“Combining FL and blockchain extends this control to model updates and data access.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > C. Federated Learning
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Rests on an assumption: Blockchain provides a secure, transparent ledger for recording transactions and maintaining data integrity.
In the paper
“Functioning as a ledger system, it organizes data into immutable and chronological blocks, authenticated through consensus mechanisms among a network of nodes, ensuring its accuracy and timeliness [50].”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > B. Blockchain
What the sources say
- Not checked yetcites [50] Konecný, 2016, Federated learning: Strategies for improving communication efficiency, arXiv:1610.05492
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
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
“The proposed model enhances patient empowerment by allowing them to maintain control over their healthcare data.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > A. Motivation and Contributions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
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 aligns with stringent regulatory requirements while boosting efficiency, transparency, and trust in the healthcare sector.”
- Also stated in: § A. Motivation and Contributions · plain
“Our framework addresses the critical balance between collaborative healthcare research and patient data privacy, aligning with stringent regulatory standards and enhancing transparency and trust in healthcare data-sharing.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > A. Motivation and Contributions
plaindirect- 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.
- Also stated in: § A. Motivation and Contributions · plain
In the paper
“In addition, decentralized data-sharing offers several benefits, such as increased privacy and security, improved data ownership and control, and enhanced transparency and accountability [8].”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > I. INTRODUCTION
What the sources say
- Not checked yetcites [8] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“FL and blockchain enhance efficient and trustworthy collaboration.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > C. Federated Learning
plaindirect- 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.
- Rests on an assumption: Blockchain provides a secure, transparent ledger for recording transactions and maintaining data integrity.
In the paper
“Together, they create a powerful platform that enhances security, privacy, interoperability, and transparency in datasharing in healthcare [62].”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > C. Federated Learning
What the sources say
- Not checked yetcites [62]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Blockchain provides a secure, transparent ledger for recording transactions and maintaining data integrity.
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
“Not only does it comply with strict regulations, but it also dramatically increases productivity and openness in the healthcare industry.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > A. Motivation and Contributions
boosteddirect- 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.
In the paper
“Advanced consensus mechanisms bolster this resilience, rendering the system less susceptible to malicious attacks or data breaches [53].”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > B. Blockchain
What the sources say
- Not checked yetcites [53] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
“Combining FL's model updates with blockchain's data record, both models and data can be verified for authenticity.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > C. Federated Learning
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
- Rests on an assumption: Blockchain provides a secure, transparent ledger for recording transactions and maintaining data integrity.
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
“Safe, effective, patient-centered datasharing will speed up medical research, enhance patient care and accelerate improvements in healthcare.”
- Also stated in: § A. Motivation and Contributions · plain
“This approach not only enhances patient care and accelerates medical research but also promises greater accuracy in diagnoses, personalized treatment options, and rapid advancements in medical science.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > A. Motivation and Contributions
plaindirect- 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.
- Also stated in: § A. Motivation and Contributions · plain
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 significant progress has been made in investigating FL and blockchain individually in healthcare, a notable gap in research exploring their synergistic potential exists.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > B. Related work
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
“We propose a novel framework combining FL and blockchain for healthcare data-sharing.This synergy addresses complex challenges related to data security and efficient sharing, ensuring a patient-centric approach.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > A. Motivation and Contributions
plaindirect- 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.
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
“The combination of blockchain technology and FL promises to transform the healthcare industry by promoting scientific breakthroughs, enhancing patient care, and guaranteeing legal compliance.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > A. Motivation and Contributions
plaindirect- 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.
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
“Such a decentralized approach leverages distributed computing for efficiency and scalability while harnessing blockchain's strengths in immutability and security.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > A. Motivation and Contributions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Combined with blockchain, a transparent and immutable ledger, it establishes an ecosystem fostering trust, security, and data integrity.”
- Also stated in: § I. INTRODUCTION · plain
“blockchain has transcended its financial origins to become a secure and immutable ledger capable of ensuring data integrity and transparency.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > Abstract
What the sources say
- Not checked yetcites [14] Nakamoto, Bitcoin: A peer-to-peer electronic cash system
- Not checked yetcites [15] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
- Also stated in: § I. INTRODUCTION · plain
In the paper
“Moreover, the synergy of FL and blockchain gives stakeholders unparalleled control over data in addition to security [17].”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > A. Motivation and Contributions
What the sources say
- Not checked yetcites [17] source
boosteddirect- 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
“Blockchain technology enables patients to participate in medical research while actively preserving data ownership.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > A. Motivation and Contributions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“Blockchain's essential attributes position it as a pivotal player in the evolution of data-sharing systems, such as Dataspace 4.0 and 6G, offering a pathway to highly secure, efficient, and transparent decentralized data-sharing platforms [52].”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > B. Blockchain
What the sources say
- Not checked yetcites [52] Li, Federated optimization in heterogeneous networks
plaindirect- 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.
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
“Our approach uniquely addresses end-to-end data security, from local model training to secure data storage and sharing, promising substantial improvements in the efficiency and trustworthiness of collaborative datasharing.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > C. Federated Learning
plaindirect- 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: Blockchain provides a secure, transparent ledger for recording transactions and maintaining data integrity.
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 with blockchain ensures end-to-end security, from data training to storage and sharing.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > C. Federated Learning
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
- Rests on an assumption: Blockchain provides a secure, transparent ledger for recording transactions and maintaining data integrity.
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
“Our proposed paradigm stands out for resolving the conventional trade-off between privacy and data-sharing.”
- Also stated in: § A. Motivation and Contributions · boosted
“Our proposed decentralized data-sharing model effectively resolves the traditional trade-off between sharing and privacy.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > A. Motivation and Contributions
boosteddirect- 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.
- Also stated in: § A. Motivation and Contributions · boosted
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's worth noting that by utilizing FL at this stage, the integrity and privacy of the hospital data is maintained.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > A. FL for Data Preprocessing and Distribution
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Rests on an assumption: A local decentralized network for healthcare data-sharing is needed and beneficial.
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 distributed processing approach is crucial for maintaining the system's integrity, ensuring data privacy, and enabling collaborative model training across various nodes.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > Publicly verifiable transactions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
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
“Each node operates autonomously within the decentralized infrastructure, conducting computations using its local data.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > IV. DECENTRALIZED DATA-SHARING IN HEALTHCARE: USE CASE
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The time complexity for such operations typically depends on the critical size and the algorithm used, often being polynomial”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > B. Sharing Iris Dataset
hedged- 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.
- Rests on an assumption: Public-key cryptography is secure for encrypting data attributes.
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 analysis, supported by the empirical data shown in Fig. 8, demonstrates that the defence strategies are sufficiently robust, and the system can be considered secure against the simulated adversarial actions.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > H. Evaluation
boosted- 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
“This not only enhances the reliability of the FL models but also ensures their functionality even in adverse circumstances.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > IV. DECENTRALIZED DATA-SHARING IN HEALTHCARE: USE CASE
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This slight increase could be due to the complexity of detecting and preventing active interception compared to the more straightforward detection of replay and identity attacks.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > H. Evaluation
hedgedconditional- 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
“The local processing at the nodes is complemented by the blockchain, which provides a secure and transparent way to record and validate the model updates contributed by each node.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > Publicly verifiable transactions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: Blockchain provides a secure, transparent ledger for recording transactions and maintaining data integrity.
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
“The FL framework ensures that processing occurs locally at each node, particularly the computationally intensive model training tasks.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > Publicly verifiable transactions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
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
“The nonce and hash checks, digital signature and IP verification, and end-to-end encryption collectively contribute to the resilience of the blockchain system, effectively mitigating the risk of poisoning attacks.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > H. Evaluation
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, it may also require additional computational resources and coordination between parties and may not always be necessary or practical depending on the specific use case.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > C. Federated Learning
hedgedconditional- 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
“This indicates the system's effective detection and prevention of replay attempts attributed to the robust verification process.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > H. Evaluation
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“Only aggregated updates are exchanged, ensuring data privacy.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > A. FL for Data Preprocessing and Distribution
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
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
“Each node in the network takes on a portion of the computational load, with local data being processed at the edge, close to the data sources.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > Publicly verifiable transactions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“A reused nonce indicates a replay attempt, which the blockchain is designed to detect.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > H. Evaluation
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These shared attributes, rather than the actual data, get recorded on the blockchain, ensuring transparency, security, and consistency.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > B. Sharing Iris Dataset
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: Blockchain provides a secure, transparent ledger for recording transactions and maintaining data integrity.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The system uses RSA-based digital signatures and IP verification to ensure the authenticity of transactions.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > H. Evaluation
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
The safe and ethical sharing of private patient data is a crucial challenge when healthcare data is expanding exponentially, and there is an increasing demand for data-driven medical advancements.
“The safe and ethical sharing of private patient data is a crucial challenge when healthcare data is expanding exponentially , and there is an increasing demand for data-driven medical advancements.”
The paper relies on this without support.
Centralized data-sharing models are increasingly seen as inadequate due to inherent privacy and security limitations, reliance on singular management entities, and accessibility challenges.
“Centralized data-sharing models, traditionally prevalent, are increasingly seen as inadequate due to their inherent privacy and security limitations, reliance on singular management entities, and accessibility challenges.”
The paper relies on this without support.
Decentralized data-sharing empowers individuals with heightened data privacy control.
“Secondly, decentralized data-sharing empowers individuals with heightened data privacy control.”
The paper relies on this without support.
Decentralized data-sharing eliminates the need for a central authority to manage data access, permitting individuals to grant access exclusively to trusted parties.
“It eliminates the need for a central authority to manage data access, permitting individuals to grant access exclusively to trusted parties.”
The paper relies on this without support.
Decentralized data-sharing improves interoperability across diverse systems and organizations by embracing open standards and protocols.
“Additionally, decentralized data-sharing improves interoperability across diverse systems and organizations.It achieves this by embracing open standards and protocols that streamline data-sharing among distinct platforms and applications.”
The paper relies on this without support.
Decentralized data-sharing enhances transparency by allowing all parties to access and validate shared data, cultivating trust and collaborative potential.
“Moreover, this decentralized approach enhances transparency by allowing all parties to access and validate shared data, cultivating trust and collaborative potential.”
The paper relies on this without support.
Blockchain technology is a formidable decentralized and distributed data-sharing solution renowned for its robust security and transparency features.
“Blockchain technology is a formidable decentralized and distributed data-sharing solution renowned for its robust security and transparency features.”
The paper relies on this without support.
Blockchain-based systems facilitate secure data exchange while preserving data integrity using common data structures and cryptographic algorithms.
“Blockchain-based systems facilitate secure data exchange while preserving data integrity using common data structures and cryptographic algorithms.”
The paper relies on this without support.
Blockchain's transparency ensures all participants maintain a shared, comprehensive view of data and its historical changes.
“Transparency, another hallmark feature of blockchain, ensures all participants maintain a shared, comprehensive view of data and its historical changes.”
The paper relies on this without support.
Blockchain's transparency fosters trust among parties, promotes accountability, and ensures compliance.
“This heightened transparency fosters trust among parties, promotes accountability, and ensures compliance.”
The paper relies on this without support.
Blockchain's resilience guarantees data availability despite system failures or network disruptions.
“Furthermore, blockchain's resilience factor is crucial in decentralized data-sharing by guaranteeing data availability despite system failures or network disruptions.”
The paper relies on this without support.
Blockchain offers secure and transparent data recording and sharing capabilities, enhances efficiency, accountability, and transparency, and presents novel solutions to industry-specific challenges.
“It offers secure and transparent data recording and sharing capabilities, enhances efficiency, accountability, and transparency, and presents novel solutions to industry-specific challenges.”
The paper relies on this without support.
Blockchain's decentralization guarantees that no single entity controls the network.
“Blockchain's decentralization guarantees that no single entity controls the network.”
The paper relies on this without support.
Blockchain's core feature is the distribution of processing among the network of nodes, which guarantees that the system is resilient to attacks, strong, and able to unite different people when trust is lacking.
“Blockchain technology's core feature is the distribution of processing among the network of nodes.It guarantees that the system is resilient to attacks, strong, and able to unite different people when trust is lacking.”
The paper relies on this without support.
Blockchain provides a secure, transparent ledger for recording transactions and maintaining data integrity.
“Blockchain complements this by providing a secure, transparent ledger for recording transactions and maintaining data integrity.”
The paper relies on this without support.
IPFS functions as a distributed file system where data is saved among several nodes.
“Instead of depending on a single server, IPFS functions as a distributed file system where data is saved among several nodes.”
The paper relies on this without support.
IPFS ensures data integrity and minimizes redundancy by assigning a unique hash to each piece of data based on its content.
“Using a content-addressed architecture, IPFS ensures data integrity and minimizes redundancy by assigning a unique hash to each piece of data depending on its content.”
The paper relies on this without support.
IPFS has improved resilience, making the system resistant to failures.
“Because the data is spread across several nodes, IPFS has improved resilience, making the system resistant to failures.”
The paper relies on this without support.
Blockchain improves security by guaranteeing data confidentiality and limiting unwanted access.
“Blockchain improves security by guaranteeing data confidentiality and limiting unwanted access.”
The paper relies on this without support.
Data immutability offers a solid basis for healthcare data exchange inside the local network because it cannot be changed once stored on the blockchain.
“Data immutability is a significant advantage as it offers a solid basis for healthcare data exchange inside the local network since it cannot be changed once data is stored on the blockchain.”
The paper relies on this without support.
A local decentralized network for healthcare data-sharing is needed and beneficial.
“The community that the local decentralized network in healthcare serves benefits greatly.First, it allows hospitals, researchers, and patients to safely and effectively share medical data, improving patient care.”
The paper relies on this without support.
Hospitals, researchers, and healthcare organizations will participate as nodes.
“Nodes representing patients, researchers, and healthcare organizations must be put up to create a local, decentralized network for sharing medical data.”
The authors state this as an assumption.
The Iris dataset is suitable for demonstrating the feasibility and effectiveness of the decentralized data-sharing mechanism.
“Given its rich history in data analysis and ML, the Iris dataset served as an ideal foundation for demonstrating the feasibility and effectiveness of our decentralized data-sharing mechanism.”
The paper relies on this without support.
The simulated attacks represent common threats to the system.
“Through these defense mechanisms, our decentralized datasharing blockchain system demonstrated resilience against the common threats posed by adversarial entities.”
The paper relies on this without support.
Public-key cryptography is secure for encrypting data attributes.
“The encryption process used for the Iris dataset attributes, like sepal length and width, is based on public-key cryptography.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Consequently, FL is not only essential in healthcare IoT networks for securing sensitive information but also as a mechanism to create a sense of security in AI applications across healthcare providers and patients [19,20].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [20]
Experimental — accuracy not yet measured
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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
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > C. Federated Learning
“However, FL does not inherently address data security during transmission.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > IV. DECENTRALIZED DATA-SHARING IN HEALTHCARE: USE CASE
“FL Encryption of data during transmission and storage Data kept on local devices”
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
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > B. Sharing Iris Dataset
“Specifically, the attributes of the Iris dataset -sepal length, sepal width, and petal length-are encrypted using the recipient's public key.”
§ Federated Learning Meets Blockchain in Decentralized Data-Sharing: Healthcare Use Case > B. Sharing Iris Dataset
“T = {encrypt(s l , pk), encrypt(s w , pk), encrypt(p l , pk), encrypt(p w , pk), species} (1)”
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Metadata
- Authors
- Alsamhi, S. H., Myrzashova, R., Hawbani, A., Kumar, S., Srivastava, S., Zhao, L., Wei, X., Guizan, M., & Curry, E.
- Affiliations
- Ollscoil na Gaillimhe – University of Galway (IE)
- University of Science and Technology of China (CN)
- Shenyang Aerospace University (CN)
- International Institute of Information Technology (IN)
- M.J.P. Rohilkhand University (IN)
- ORCID
- Venue
- IEEE Internet of Things Journal
- Published
- 2024-06-01
- Volume
- 11
- Issue
- 11
- Pages
- 19602-19615
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W4391948475
- Semantic Scholar
- d0455754ac1f9ff5de3bd5ce76f3a38b40322849
- Cited by
- 131
- References
- 56
- Citations by year
- 2026: 48 · 2025: 60 · 2024: 23
- FWCI
- 49.26
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE Internet of Things Journal (publishedVersion, cc-by)
- Funders
- Science Foundation Ireland (SFI/12/RC/2289_P2)
- ANSO Scholarship for Young Talents
- Natural Science Foundation of Anhui Province (BJ2060000039)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology in Education and Learning
- Keywords
- federated learning, blockchain, decentralized data sharing, healthcare data privacy, patient data ownership, data integrity, patient empowerment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
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