Enhancing Robustness within the Collaborative Federated Learning Framework: A Novel Grouping Algorithm for Edge Clients
Source abstract
Abstract
In this study, we introduce a novel collaborative federated learning (FL) framework, aiming at enhancing robustness in distributed learning environments, particularly pertinent to IoT and industrial automation scenarios. At the core of our contribution is the development of an innovative grouping algorithm for edge clients. This algorithm employs a distinctive ID distribution function, enabling efficient and secure grouping of both normal and potentially malicious clients. Our proposed grouping scheme accurately determines the numerical difference between normal and malicious groups under various network scenarios. Our method addresses the challenge of model poisoning attacks, ensuring the accuracy of outcomes in a collaborative federated learning framework. Our numerical experiments demonstrate that our grouping scheme effectively limits the number of malicious groups. Additionally, our collaborative FL framework has shown resilience against various levels of poisoning attack abilities and maintained high prediction accuracy across a range of scenarios, showcasing its robustness against poisoning attacks.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Privacy-Preserving Technologies in Data · Network Security and Intrusion Detection · Internet Traffic Analysis and Secure E-voting
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Multimedia and Game Development, Chia-Nan University of Pharmacy and Science, No. 60, Sec. 1, Erren Rd., Rende Dist., Tainan City 717301, Taiwan
Department of Electrical Engineering, Institute of Computer and Communication Engineering, National Cheng Kung University, No. 1, University Rd., East Dist., Tainan City 701401, Taiwan
Department of Finance, National Formosa University, No. 64, Wunhua Rd., Huwei Township, Yunlin County 632301, Taiwan
Department of Artificial Intelligence and Computer Engineering, National Chin-Yi University of Technology, No. 57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung 411030, Taiwan
Department of Information Management, Chia-Nan University of Pharmacy and Science, No. 60, Sec. 1, Erren Rd., Rende Dist., Tainan City 717301, Taiwan
Funding
National Science and Technology Council (NSTC) in Taiwan
References (25)
Show source-provided references
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