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A Blockchain-based Decentralized Federated Learning Framework with Dual-Committees Consensus

Published: 14 March 2023 Publication History

Abstract

Federated learning has gotten more and more attention in recent years. At the same time, blockchain is fast becoming a key instrument for achieving decentralization. The combination of blockchain and federated learning is becoming a hot topic of research. The general architecture for blockchain-based federated learning is to replace the central server with the blockchain with a single committee directly. However, the absence of partial nodes in the committee will make the distribution of samples uneven. In this paper, we introduce a blockchain-based decentralized federated learning Framework(BFLDC), and BFLDC creatively introduces the dual-committees. Different from the single committee, our dual-committees can guarantee to choose the good nodes and the others to construct the committee, which can make more nodes into the committee, as well as prevent the training process from the attack of the malicious nodes. Experimental results demonstrate the proposed framework can achieve the preset goals.

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Cited By

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  • (2024)A Framework to Design Efficent Blockchain-Based Decentralized Federated Learning ArchitecturesIEEE Open Journal of the Computer Society10.1109/OJCS.2024.34885125(705-723)Online publication date: 2024
  • (2024)FedBlock: A Blockchain Approach to Federated Learning against Backdoor Attacks2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825703(7981-7990)Online publication date: 15-Dec-2024

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cover image ACM Other conferences
ACAI '22: Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence
December 2022
770 pages
ISBN:9781450398336
DOI:10.1145/3579654
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

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Published: 14 March 2023

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  1. blockchain
  2. federated learning

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ACAI 2022

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Overall Acceptance Rate 173 of 395 submissions, 44%

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View all
  • (2024)A Framework to Design Efficent Blockchain-Based Decentralized Federated Learning ArchitecturesIEEE Open Journal of the Computer Society10.1109/OJCS.2024.34885125(705-723)Online publication date: 2024
  • (2024)FedBlock: A Blockchain Approach to Federated Learning against Backdoor Attacks2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825703(7981-7990)Online publication date: 15-Dec-2024

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