Research progress of blockchain‑based federated learning

doi:10.11772/j.issn.1001-9081.2021111934

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (11): 3413-3420.DOI: 10.11772/j.issn.1001-9081.2021111934

Special Issue: 2021 CCF中国区块链技术大会（CCF CBCC 2021）

• 2021 CCF China Blockchain Conference （CCF CBCC 2021） • Previous Articles Next Articles

Research progress of blockchain‑based federated learning

Rui SUN¹^,², Chao LI¹^,²(), Wei WANG¹^,², Endong TONG¹^,², Jian WANG¹^,², Jiqiang LIU¹^,²

^1.Beijing Key Laboratory of Security and Privacy in Intelligent Transportation （Beijing Jiaotong University），Beijing 100044，China
^2.School of Computer and Information Technology，Beijing Jiaotong University，Beijing 100044，China

Received:2021-11-14 Revised:2021-12-08 Accepted:2021-12-23 Online:2022-01-19 Published:2022-11-10
Contact: Chao LI
About author:SUN Rui， born in 1998， M. S. candidate. Her research interests include blockchain.
LI Chao， born in 1988， Ph. D.， lecturer. His research interests include blockchain.
WANG Wei， born in 1976， Ph. D.， professor. His research interests include network and system， industrial Internet， blockchain.
TONG Endong， born in 1986， Ph. D.， lecturer. His research interests include service computing， artificial intelligence.
WANG Jian， born in 1975， Ph. D.， associate professor. His research interests include password application， blockchain， network security.
LIU Jiqiang， born in 1973， Ph. D.， professor. His research interests include trusted computing， privacy protection， Internet of Things.
Supported by:
National Key Research and Development Program of China(2020YFB2103802);Fundamental Research Funds for Central Universities(2019RC038)

基于区块链的联邦学习研究进展

孙睿¹^,², 李超¹^,²(), 王伟¹^,², 童恩栋¹^,², 王健¹^,², 刘吉强¹^,²

^1.智能交通数据安全与隐私保护技术北京市重点实验室(北京交通大学), 北京 100044
^2.北京交通大学计算机与信息技术学院, 北京 100044

通讯作者: 李超
作者简介:孙睿（1998—），女，吉林扶余人，硕士研究生，主要研究方向：区块链
李超（1988—），男，甘肃天水人，讲师，博士，CCF会员，主要研究方向：区块链 li.chao@bjtu.edu.cn
王伟（1976—），男，湖北英山人，教授，博士，主要研究方向：网络与系统、工业互联网、区块链
童恩栋（1986—），男，山东聊城人，讲师，博士，主要研究方向：服务计算、人工智能
王健（1975—），男，山东烟台人，副教授，博士，主要研究方向：密码应用、区块链、网络安全
刘吉强（1973—），男，山东烟台人，教授，博士，主要研究方向：可信计算、隐私保护、物联网。
基金资助:
国家重点研发计划项目(2020YFB2103802);中央高校基本科研业务费专项资金资助项目(2019RC038)

Abstract

Abstract:

Federated Learning （FL） is a novel privacy?preserving learning paradigm that can keep users' data locally. With the progress of the research on FL， the shortcomings of FL， such as single point of failure and lack of credibility， are gradually gaining attention. In recent years， the blockchain technology originated from Bitcoin has achieved rapid development， which pioneers the construction of decentralized trust and provides a new possibility for the development of FL. The existing research works on blockchain?based FL were reviewed， the frameworks for blockchain?based FL were compared and analyzed. Then， key points of FL solved by the combination of blockchain and FL were discussed. Finally， the application prospects of blockchain?based FL were presented in various fields， such as Internet of Things （IoT）， Industrial Internet of Things （IIoT）， Internet of Vehicles （IoV） and medical services.

Key words: Federated Learning (FL), blockchain, structural framework, fusion application, privacy protection

摘要：

联邦学习（FL）是一种能够实现用户数据不出本地的新型隐私保护学习范式。随着相关研究工作的不断深入，FL的单点故障及可信性缺乏等不足之处逐渐受到重视。近年来，起源于比特币的区块链技术取得迅速发展，它开创性地构建了去中心化的信任，为FL的发展提供了一种新的可能。对现有基于区块链的FL框架进行对比分析，深入讨论区块链与FL相结合所解决的FL重要问题，并阐述了基于区块链的FL技术在物联网（IoT）、工业物联网（IIoT）、车联网（IoV）、医疗服务等多个领域的应用前景。

关键词: 联邦学习, 区块链, 结构框架, 融合应用, 隐私保护

CLC Number:

TP309.2

Rui SUN, Chao LI, Wei WANG, Endong TONG, Jian WANG, Jiqiang LIU. Research progress of blockchain‑based federated learning[J]. Journal of Computer Applications, 2022, 42(11): 3413-3420.

孙睿, 李超, 王伟, 童恩栋, 王健, 刘吉强. 基于区块链的联邦学习研究进展[J]. 《计算机应用》唯一官方网站, 2022, 42(11): 3413-3420.

Figures/Tables 5

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结构	种类	共识机制	激励机制
BlockFL^［13］	公链	PoW	数据和挖矿奖励
FL‑Block^［14］	公链	PoW	数据和挖矿奖励
BFL^［15］	公链	PoW	数据和挖矿奖励
BFL^［20］	私链	代理权益证明	行动的影响
FLChain^［18］	公链	拜占庭/PoW	—
BFEL^［21］	公链/联盟链	验证证明	贡献
BFLC^［1］	联盟链	CCM	根据贡献利益共享
FL^［26］	联盟链	权益证明/拜占庭	多重Krum和声誉

结构	种类	共识机制	激励机制
BlockFL^［13］	公链	PoW	数据和挖矿奖励
FL‑Block^［14］	公链	PoW	数据和挖矿奖励
BFL^［15］	公链	PoW	数据和挖矿奖励
BFL^［20］	私链	代理权益证明	行动的影响
FLChain^［18］	公链	拜占庭/PoW	—
BFEL^［21］	公链/联盟链	验证证明	贡献
BFLC^［1］	联盟链	CCM	根据贡献利益共享
FL^［26］	联盟链	权益证明/拜占庭	多重Krum和声誉

解决的问题	解决方法
激励机制	数据奖励和挖矿奖励^［13-15］
	贡献利益共享^［1］
	多重Krum计算声誉^［26］
	智能合约^［24］
投毒攻击	验证证明共识机制^［21］
	多重Krum共识机制^［30］
	认知计算^［28］
	计分机制^［32］
提高学习效率	从边缘数据学习模型的异步学习^［20］
提高模型验证效率	委员会共识机制^［1］
提高通信效率	梯度压缩^［21］
提高模型训练效率	超参数优化和弹性权重合并^［37］

解决的问题	解决方法
激励机制	数据奖励和挖矿奖励^［13-15］
	贡献利益共享^［1］
	多重Krum计算声誉^［26］
	智能合约^［24］
投毒攻击	验证证明共识机制^［21］
	多重Krum共识机制^［30］
	认知计算^［28］
	计分机制^［32］
提高学习效率	从边缘数据学习模型的异步学习^［20］
提高模型验证效率	委员会共识机制^［1］
提高通信效率	梯度压缩^［21］
提高模型训练效率	超参数优化和弹性权重合并^［37］

应用领域	功能
物联网	保证数据安全^［22］
	工业物联网的故障检测^［24］
	优化家电功能^［26］
医疗服务	保护病人隐私^［40］
	预测糖尿病风险^［41］
	医疗物联网设备的隐私算法^［42］
	新冠病毒诊断共享^［43］
	医疗信息保护和共享^［44］
	提高新冠病毒识别率^［45］
车联网	预测自动车辆间的有效通信^［15］
	车辆运行数据共享^［20］
	提高车内网络可靠性和安全性^［48］
内容缓存	提高缓存命中率^［49］
位置预测	更准确的位置预测和隐私保护^［50］
移动众感知	保护无人机学习后的隐私^［51］
灾难响应	无人机和6G网络灾难响应系统^［52］
新闻推荐	过滤信息，精准推荐，隐私保护^［53］

Research progress of blockchain‑based federated learning

基于区块链的联邦学习研究进展

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