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A verifiable and privacy-preserving blockchain-based federated learning approach

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Abstract

Federated Learning (FL) is a promising approach to enabling machine learning on decentralized data. It allows multiple clients to train a global model without transferring their data to a central server. However, traditional federated learning suffers from privacy and security problems due to the potential leakage of sensitive information. Existing consensus algorithms such as Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS) etc., are not scalable and efficient for permissioned blockchain networks. In this paper, we propose a blockchain-based federated learning approach using the Proof of Authority (PoA) consensus algorithm to address these issues. The proposed framework leverages the immutability and transparency of blockchain to ensure the integrity and privacy of the data during the federated learning process. We evaluate the proposed blockchain-based FL approach on a simulated dataset, and the results show that it achieves a higher level of accuracy, efficiency, privacy and security compared to existing approaches. We also compare the PoA consensus algorithm with other consensus algorithms. The proposed approach, Blockchain-based Federated Learning (BC-FL) is designed to be more communication efficient, scalable, and secure than existing approaches in blockchain-based FL systems.

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Funding

This work was supported in part by the Na- tional Natural Science Foundation of China under Grant 62172441, Grant 62172449, and Grant 61772553, in part by the Local Science and Technology Developing Foundation Guided by Central Government under Free Exploration Grant 2021Szvup166, and in part by the Opening Project of State Key Laboratory of Nickel and Cobalt Resources Comprehensive Utilization under Grant GZSYS-KY-2022-018 and Grant GZSYS-KY-2022–024.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, 410083, China

    Irshad Ullah, Xiaoheng Deng, Xinjun Pei, Ping Jiang & Husnain Mushtaq

Authors
  1. Irshad Ullah

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  2. Xiaoheng Deng

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  3. Xinjun Pei

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  4. Ping Jiang

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  5. Husnain Mushtaq

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Contributions

Irshad Ullah conceptualized the research goals and aims, designed the technical framework. He made a significant intellectual contribution to the presentation of the research work, specifically writing the initial draft. Xiaoheng Deng made a significant intellectual contribution to the theoretical development and system. Specifically, he is responsibility for the research activity planning and execution, including mentorship to the core team. Xinjun Pei, Ping Jiang, and Husnain Mushtaq made a significant intellectual contribution to the establishment of methodology and implementation of the computer code and supporting algorithms.

Corresponding author

Correspondence toXiaoheng Deng.

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This article is part of the Topical Collection:2 - Track on Security and Privacy

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Ullah, I., Deng, X., Pei, X.et al. A verifiable and privacy-preserving blockchain-based federated learning approach.Peer-to-Peer Netw. Appl.16, 2256–2270 (2023). https://doi.org/10.1007/s12083-023-01531-8

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