Federated Learning Meets Blockchain in Decentralized Data Sharing: Healthcare Use Case

Abstract

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.

Exploring the Synergy of Fog Computing, Blockchain, and Federated Learning for IoT Applications: A Systematic Literature Review

“…As Table 1 shows, prevailing studies have predominantly focused on examining combinations of two out of the three technologies: BC-FC [11]- [15], FC-FL [16]- [18], or BC-FL [16], [19]- [26]. A noticeable trend emerges wherein there is a scarcity of literature reviews encompassing the integration of all three technologies comprehensively.…”

“…By bridging this crucial gap in the literature, our review offers unparalleled insights that are indispensable for advancing research and practical applications in this field. Analysis of the studies [11] 2022 FC-BC [12] 2023 FC/EC-BC [13] 2019 FC/EC-BC [14] 2020 FC-BC [15] 2019 FC-BC [16] 2021 FC-FL/BC-FL [17] 2021 FC-FL [18] 2020 FC-FL [19] 2021 BC-FL [20] 2022 BC-FL [21] 2021 BC-FL [22] 2021 BC-FL [23] 2022 BC-FL [24] 2021 BC-FL [25] 2022 BC-FL [26] 2024 BC-FL Our Review FC/EC-BC-FL…”

“…Consensus is regulated by pre-authorized nodes, and reading rights can be public or limited. However, controlled consortium BCs risk tampering if nodes permit [20], [21], [24], [26], [40]. d) Hybrid: It combines public and private features, offering flexibility and balance in data sharing and control.…”

“…1) Field of application. It encompasses various typical application fields, including Business/Finance Management, Energy, Governance, Healthcare, Industrial Management, Smart Cities/Smart Homes, Transport, and others [17], [26], [50]- [52] (see Figure 7). 2) Scenario.…”

“…Numerous secondary studies (or literature reviews) have recognized that despite the collaborative advancement of these technologies within IoT solutions, persistent challenges endure. Yet, these reviews tend to focus primarily on pairs of technologies: BC and FC [11]- [15], FC and FL [16]- [18], or BC and FL [16], [19]- [26]. While primary studies are emerging to explore the direct relationship among all three technologies, there is a noticeable lack of secondary studies summarizing key findings and issues across FC, BC, and FL, highlighting a substantial research gap.…”