Secure Data Sharing in Federated Learning through Blockchain-Based Aggregation

Bowen Liu and Qiang Tang ()
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Bowen Liu: Luxembourg Institute of Science and Technology (LIST), 5, Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
Qiang Tang: Luxembourg Institute of Science and Technology (LIST), 5, Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg

Future Internet, 2024, vol. 16, issue 4, 1-13

Abstract: In this paper, we explore the realm of federated learning (FL), a distributed machine learning (ML) paradigm, and propose a novel approach that leverages the robustness of blockchain technology. FL, a concept introduced by Google in 2016, allows multiple entities to collaboratively train an ML model without the need to expose their raw data. However, it faces several challenges, such as privacy concerns and malicious attacks (e.g., data poisoning attacks). Our paper examines the existing EIFFeL framework, a protocol for decentralized real-time messaging in continuous integration and delivery pipelines, and introduces an enhanced scheme that leverages the trustworthy nature of blockchain technology. Our scheme eliminates the need for a central server and any other third party, such as a public bulletin board, thereby mitigating the risks associated with the compromise of such third parties.

Keywords: federated learning; privacy-preserving; blockchain (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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