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Peak-controlled logits poisoning attack in federated distillation

文献类型：期刊论文

作者Tang, Yuhan3,4; Wu, Zhiyuan1,2; Gao, Bo3,4; Wen, Tian2; Wang, Yuwei2; Sun, Sheng2
刊名DISCOVER COMPUTING
出版日期2025-10-22
卷号28期号:1页码:18
关键词Federated learning Knowledge distillation Knowledge transfer Poisoning attack Misleading attack
ISSN号2948-2984
DOI10.1007/s10791-025-09742-8
英文摘要Federated Distillation (FD) is an innovative distributed machine learning paradigm that enables efficient and flexible cross-device knowledge transfer through knowledge distillation, without the need to upload large-scale model parameters to a central server. Although FD has attracted increasing attention in recent years, its security aspects remain relatively underexplored. Existing attack methods targeting traditional federated learning mainly focus on the transmission of model parameters and gradients, while attacks specifically designed for the unnormalized outputs (logits) in the emerging FD paradigm are still lacking. To fill this research gap and contribute to the enhancement of FD's security, we previously proposed the Federated Distillation Logits Attack (FDLA), which manipulates the logits transmitted during communication to mislead and degrade the performance of client models. However, FDLA has limitations in controlling its impact on participants with different roles or identities and lacks a systematic investigation into the effects of malicious interventions at various stages of knowledge transfer. To overcome these limitations, we propose a more advanced and controllable logits poisoning method-Peak-Controlled Federated Distillation Logits Attack (PCFDLA). PCFDLA enhances the effectiveness of FDLA by precisely controlling the peak values of logits to adjust the intensity of the attack. This method generates highly misleading perturbations that achieve stronger attack performance while maintaining a similar level of stealthiness to FDLA when detection is based on differences in model parameters. Moreover, we introduce a novel evaluation metric to more comprehensively assess the performance of such attacks. Experimental results show that PCFDLA significantly increases the destructive impact on victim models while maintaining high stealth. It consistently achieves superior performance across multiple datasets, highlighting its potential threat to the security of federated distillation systems.
资助项目Fundamental Research Funds for the Central Universities
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001598387200001
出版者SPRINGER
源URL[http://119.78.100.204/handle/2XEOYT63/41632]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Gao, Bo
作者单位1.Univ Chinese Acad Sci, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
3.Beijing Jiaotong Univ, Collaborat Innovat Ctr Railway Traff Safety, Beijing, Peoples R China
4.Beijing Jiaotong Univ, Engn Res Ctr Network Management Technol High Speed, Sch Comp Sci & Technol, Minist Educ, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Tang, Yuhan,Wu, Zhiyuan,Gao, Bo,et al. Peak-controlled logits poisoning attack in federated distillation[J]. DISCOVER COMPUTING,2025,28(1):18.
APA Tang, Yuhan,Wu, Zhiyuan,Gao, Bo,Wen, Tian,Wang, Yuwei,&Sun, Sheng.(2025).Peak-controlled logits poisoning attack in federated distillation.DISCOVER COMPUTING,28(1),18.
MLA Tang, Yuhan,et al."Peak-controlled logits poisoning attack in federated distillation".DISCOVER COMPUTING 28.1(2025):18.

入库方式： OAI收割

来源：计算技术研究所

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