Decentralized Defense: Leveraging Blockchain against Poisoning Attacks in Federated Learning Systems
Thennakoon, Rashmi; Wanigasundara, Arosha; Weerasinghe, Sanjaya; Seneviratne, Chatura; Siriwardhana, Yushan; Liyanage, Madhusanka (2024-03-18)
IEEE
18.03.2024
R. Thennakoon, A. Wanigasundara, S. Weerasinghe, C. Seneviratne, Y. Siriwardhana and M. Liyanage, "Decentralized Defense: Leveraging Blockchain against Poisoning Attacks in Federated Learning Systems," 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 2024, pp. 950-955, doi: 10.1109/CCNC51664.2024.10454688
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202405294059
https://urn.fi/URN:NBN:fi:oulu-202405294059
Tiivistelmä
Abstract
Federated learning (FL) has become the next generation of machine learning (ML) by avoiding local data sharing with a central server. While this becomes a major advantage to client-side privacy, it has a trade-off of becoming vulnerable to poisoning attacks and malicious behavior of the central server. As the decentralization of systems enhances security concerns, integrating decentralized defense for the existing FL systems has been extensively studied to eliminate the security issues of FL systems. This paper proposes a decentralized defense approach to FL systems with blockchain technology to overcome the poisoning attack without affecting the existing FL system's performance. We introduce a reliable blockchain-based FL (BCFL) architecture in two different models, namely, Centralized Aggregated BCFL (CA-BCFL) and Fully Decentralized BCFL (FD-BCFL). Both models utilize secure off-chain computations for malicious mitigation as an alternative to high-cost on-chain computations. Our comprehensive analysis shows that the proposed BCFL architectures can defend in a similar manner against poisoning attacks that compromise the aggregator. As a better measure, the paper has included an evaluation of the gas consumption of our two system models.
Federated learning (FL) has become the next generation of machine learning (ML) by avoiding local data sharing with a central server. While this becomes a major advantage to client-side privacy, it has a trade-off of becoming vulnerable to poisoning attacks and malicious behavior of the central server. As the decentralization of systems enhances security concerns, integrating decentralized defense for the existing FL systems has been extensively studied to eliminate the security issues of FL systems. This paper proposes a decentralized defense approach to FL systems with blockchain technology to overcome the poisoning attack without affecting the existing FL system's performance. We introduce a reliable blockchain-based FL (BCFL) architecture in two different models, namely, Centralized Aggregated BCFL (CA-BCFL) and Fully Decentralized BCFL (FD-BCFL). Both models utilize secure off-chain computations for malicious mitigation as an alternative to high-cost on-chain computations. Our comprehensive analysis shows that the proposed BCFL architectures can defend in a similar manner against poisoning attacks that compromise the aggregator. As a better measure, the paper has included an evaluation of the gas consumption of our two system models.
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