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arXiv 2205.10568cs.DC
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BlockDFL:一种基于区块链的完全去中心化对等联邦学习框架

BlockDFL: A Blockchain-based Fully Decentralized Peer-to-Peer Federated Learning Framework

Zhen Qin, Xueqiang Yan, Mengchu Zhou, Shuiguang Deng

AI总结:

针对P2P联邦学习中的投毒、泄露与高通信开销问题,提出基于区块链、PBFT投票、两层评分和梯度压缩的BlockDFL框架,在40%恶意节点下仍保持准确率。

摘要:

联邦学习(FL)能够在不共享训练数据的情况下协同训练机器学习模型。传统FL严重依赖可信的中央服务器。尽管去中心化FL消除了对中心的依赖,但由于对参与者行为缺乏足够约束,它可能会加剧FL面临的其他固有问题,例如投毒攻击和数据表征泄露,并且带来沉重的通信开销,尤其是在完全去中心化场景(即对等P2P设置)中。本文提出一种基于区块链的完全去中心化P2P联邦学习框架,名为BlockDFL。该框架以区块链为基础,利用所提出的基于PBFT的投票机制和两层评分机制,在互不信任的对等参与者之间协调FL,同时有效防御投毒攻击。框架引入梯度压缩以降低通信成本,并防止数据从传输的模型更新中被重构。在两个真实数据集上进行的大量实验表明,与中心化FL相比,BlockDFL获得了具有竞争力的准确率,并能在实现效率和可扩展性的同时防御投毒攻击。特别是当恶意参与者比例高达40%时,BlockDFL仍能保持FL的准确率,优于现有基于区块链的完全去中心化P2P FL框架。

英文摘要:

Federated learning (FL) enables collaborative training of machine learning models without sharing training data. Traditional FL heavily relies on a trusted centralized server. Although decentralized FL eliminates the central dependence, it may worsen the other inherit problems faced by FL such as poisoning attacks and data representation leakage due to insufficient restrictions on the behavior of participants, and heavy communication cost, especially in fully decentralized scenarios, i.e., peer-to-peer (P2P) settings. In this paper, we propose a blockchain-based fully decentralized P2P framework for FL, called BlockDFL. It takes blockchain as the foundation, leveraging the proposed PBFT-based voting mechanism and two-layer scoring mechanism to coordinate FL among peer participants without mutual trust, while effectively defending against poisoning attacks. Gradient compression is introduced to lowering communication cost and prevent data from being reconstructed from transmitted model updates. Extensive experiments conducted on two real-world datasets exhibit that BlockDFL obtains competitive accuracy compared to centralized FL and can defend poisoning attacks while achieving efficiency and scalability. Especially when the proportion of malicious participants is as high as 40%, BlockDFL can still preserve the accuracy of FL, outperforming existing fully decentralized P2P FL frameworks based on blockchain.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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