基于区块链的联邦学习研究综述

doi:10.3969/j.issn.1671-1122.2024.11.004

摘要/Abstract

摘要：

基于区块链的联邦学习作为一种新兴的去中心化的分布式机器学习新范式，其在克服传统联邦学习所面临的数据孤岛、隐私泄露以及安全威胁等不足的同时，也面临着区块链技术在成本、效率以及有效性等方面带来的新挑战。为此，文章首先结合基本原理、技术分类、优势以及待解决问题对联邦学习和区块链进行阐述。在此基础上，文章围绕联邦学习与区块链所涉及的架构、性能、隐私性、安全性、激励机制、共识机制等对基于区块链的联邦学习研究进行了系统的总结分析。最后，文章从基于区块链的联邦学习原理、平衡性以及应用三个维度，探讨未来的研究趋势和亟待解决的主要问题。

关键词: 机器学习, 区块链, 联邦学习, 去中心化, 分布式

Abstract:

As an emerging decentralized distributed machine learning paradigm, blockchain based federated learning not only overcomes the shortcomings of traditional federated learning such as data silos, privacy breaches, and security threats, but also faces new challenges in terms of cost, efficiency, and effectiveness brought by blockchain technology. Therefore, this article first elaborated on federated learning and blockchain by combining basic principles, technical classifications, complementary advantages, and unresolved problems. On this basis, a systematic summary and analysis of current research on blockchain based federated learning was conducted around the architecture, performance, privacy, security, incentive mechanisms, consensus mechanisms, and applications involved in the combination of federated learning and blockchain. Finally, starting from the three dimensions of blockchain based federated learning itself, balance, and application, explored its future research trends and the main problems that urgently need to be solved.

Key words: machine learning, blockchain, federated learning, decentralization, distributed

中图分类号:

TP309

兰浩良, 王群, 徐杰, 薛益时, 张勃. 基于区块链的联邦学习研究综述[J]. 信息网络安全, 2024, 24(11): 1643-1654.

LAN Haoliang, WANG Qun, XU Jie, XUE Yishi, ZHANG Bo. Review of Research on Blockchain-Based Federated Learning[J]. Netinfo Security, 2024, 24(11): 1643-1654.

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参考文献 78

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指标分类	私有链	公有链	联盟链
成员	机构内部	任何人	联盟内部
共识机制	Paxos、Raft、 Pbft等	PoW、Pbft、 Rpca等	PoS、Raft、 Pbft等
记账人	所有成员	自定义	协商指定
激励机制	缺乏	有必要	可选
优势	可追溯+透明	自建立信用	效率+成本
中心化程度	完全去中心化	中心化	部分去中心化
承载力	强（<20万笔/秒）	弱（<20笔/秒）	较强（<1万笔/秒）
典型应用	审计、预算、监管	清算、货币加密、投票	供应链、溯源、支付

序号	攻击方式	主要特点	防御手段	文献(近5年)
1	单点故障	影响模型聚合、消耗大量资源	错误率、损失函数、节点共识	[22??-25]
2	拒绝服务	瘫痪中央服务器或整个系统	拜占庭共识、知识图谱、知识库	[21,26]
3	搭便车	影响效率、有效性、公平性	审查、异常值检测、高斯混合模型	[27?-29]
4	投毒	影响训练和聚合的准确性	交叉检测、节点选择、梯度选择	[24,25,30,31]
5	中间人	欺骗会话双方、劫持会话信息	签名、临时聚合器、安全通道	[32?-34]
6	窃听	导致敏感信息泄露或网络中断	梯度稀疏、差分隐私、同态加密	[24,35,36]
7	后门	操纵模型在特定输入下的输出	对比训练、范数阈值、触发器	[37,38]

序号	共识机制	能耗	优势	不足
1	PoW	高	去中心化度、节点进出、安全性、扩展性	资源消耗、共识周期
2	PoS	适中	资源消耗、共识速度、块生成效率	中心化、实现、安全性
3	DPoW	低	安全性、共识速度、验证和记账节点	去中心化度、效率
4	DPoS	低	验证和记账节点、共识速度、资源消耗	公平性差、依赖令牌
5	Pbft	低	资源消耗、吞吐量、共识效率、交易频次	复杂度、扩展性、节点数
6	Paxos	低	性能、资源消耗、代币需求	容错性、实现
7	Raft	低	资源消耗、共识效率、可用性、一致性	容错性
8	Pool	适中	代币需求、共识速度	去中心化度
9	CoPC	低	共识效率、考虑贡献度、隐私性	复杂度、效率
10	PF-PoFL	高	性能、隐私性、安全性	资源消耗、共识周期
11	PoQ	低	数据共享相率、资源利用率	隐私性、安全性

序号	应用领域	主要优势	文献(近5年)
1	医疗健康	数据安全、可信共享、可审计性、激励机制	[52???-56]
2	物联网	准确性、安全性、隐私性、有效性	[16,50,57?-59]
3	车联网	隐私性、安全性、数据采集、通信效率	[60??-63]
4	无人机	有效性、安全性、隐私性	[64,65]
5	智能运输	安全性、隐私性、运输效率	[66,67]
6	5G&6G	可靠性、安全性、通信效率、准确性	[68?-70]
7	边缘计算	带宽优化、隐私保护、通信效率	[71?-73]
8	雾计算	高效率、安全性、隐私性	[74,75]
9	认知计算	可验证性、快速收敛	[76,77]