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SPFL: A Self-Purified Federated Learning Method Against Poisoning Attacks

文献类型：期刊论文

作者Liu, Zizhen1,2,3; He, Weiyang4; Chang, Chip-Hong4; Ye, Jing1,2,3; Li, Huawei1,2,3; Li, Xiaowei1,2,3
刊名IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
出版日期2024
卷号19页码:6604-6619
关键词Data models Servers Training Hidden Markov models Training data Adaptation models Security Federated learning poisoning attack knowledge distillation attention maps deep neural network
ISSN号1556-6013
DOI10.1109/TIFS.2024.3420135
英文摘要While Federated learning (FL) is attractive for pulling privacy-preserving distributed training data, the credibility of participating clients and non-inspectable data pose new security threats, of which poisoning attacks are particularly rampant and hard to defend without compromising privacy, performance or other desirable properties. In this paper, we propose a self-purified FL (SPFL) method that enables benign clients to exploit trusted historical features of locally purified model to supervise the training of aggregated model in each iteration. The purification is performed by an attention-guided self-knowledge distillation where the teacher and student models are optimized locally for task loss, distillation loss and attention loss simultaneously. SPFL imposes no restriction on the communication protocol and aggregator at the server. It can work in tandem with any existing secure aggregation algorithms and protocols for augmented security and privacy guarantee. We experimentally demonstrate that SPFL outperforms state-of-the-art FL defenses against poisoning attacks. The attack success rate of SPFL trained model remains the lowest among all defense methods in comparison, even if the poisoning attack is launched in every iteration with all but one malicious clients in the system. Meantime, it improves the model quality on normal inputs compared to FedAvg, either under attack or in the absence of an attack.
资助项目National Research Foundation, Singapore[NRF2018NCR-NCR009-0001] ; Ministry of Education, Singapore[MOE-T2EP20121-0008] ; National Natural Science Foundation of China (NSFC)[92373206] ; National Natural Science Foundation of China (NSFC)[U20A20202] ; Youth Innovation Promotion Association Chinese Academy of Sciences (CAS)
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001270320400001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/39639]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Chang, Chip-Hong
作者单位1.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, State Key Lab Processors, Beijing 100190, Peoples R China
3.CASTEST Co Ltd, Beijing 100190, Peoples R China
4.Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore
推荐引用方式
GB/T 7714		 Liu, Zizhen,He, Weiyang,Chang, Chip-Hong,et al. SPFL: A Self-Purified Federated Learning Method Against Poisoning Attacks[J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,2024,19:6604-6619.
APA Liu, Zizhen,He, Weiyang,Chang, Chip-Hong,Ye, Jing,Li, Huawei,&Li, Xiaowei.(2024).SPFL: A Self-Purified Federated Learning Method Against Poisoning Attacks.IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,19,6604-6619.
MLA Liu, Zizhen,et al."SPFL: A Self-Purified Federated Learning Method Against Poisoning Attacks".IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 19(2024):6604-6619.

入库方式： OAI收割

来源：计算技术研究所

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