Federated learning (FL) is an emerging paradigm that allows participants to collaboratively train deep learning tasks while protecting the privacy of their local data. However, the absence of central server control in distributed environments exposes a vulnerability to data poisoning attacks, where adversaries manipulate the behavior of compromised clients by poisoning local data. In particular, data poisoning attacks against FL can have a drastic impact when the participant’s local data is non-independent and identically distributed (non-IID). Most existing defense strategies have demonstrated promising results in mitigating FL poisoning attacks, however, fail to maintain their effectiveness with non-IID data. In this work, we propose an effective defense framework, FL data augmentation (FLDA), which defends against data poisoning attacks through local data mixup on the clients. In addition, to mitigate the non-IID effect by exploiting the limited local data, we propose a gradient detection strategy to reduce the proportion of malicious clients and raise benign clients. Experimental results on datasets show that FLDA can effectively reduce the poisoning success rate and improve the global model training accuracy under poisoning attacks for non-IID data. Furthermore, FLDA can increase the FL accuracy by more than 12% after detecting malicious clients.

Chunyong Yin

Qingkui Zeng

IEEE Transactions on Computational Social Systems

IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.

The IEEE Transactions on Computational Social Systems focuses on modeling, simulation, analysis, and understanding of social systems from quantitative and/or computational perspectives. "Systems" encompass man-man, man-machine, and machine-machine organizations, adversarial situations, as well as social media structures and their dynamics. Specifically, the journal publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. It also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, computational behavior modeling, and their applications.

《IEEE计算社会系统汇刊》专注于从定量和/或计算的角度对社会系统进行建模、仿真、分析和理解。这里的“系统”包括人际关系、人与机器、机器与机器之间的组织和对抗情境，以及社交媒体结构及其动态。具体而言，该期刊发表关于社会系统动态建模、将社会文化和行为方面纳入计算建模的方法论、社会系统行为和结构分析，以及社会系统建模和仿真的范式的文章。期刊还涵盖社交网络动态、社会智能与认知、社会系统设计与架构、社会文化建模与表现、计算行为建模及其应用等主题的文章。

Category	Quartile
计算机科学	2
计算机科学, 计算机信息系统	2
计算机科学, 计算机控制论	3

Defending Against Data Poisoning Attack in Federated Learning With Non-IID Data

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Journal Info

Category	Quartile
COMPUTER SCIENCE, INFORMATION SYSTEMS	1