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Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective by Zhang, Zhongjian; Zhang, Mengmei; Wang, Xiao; Lyu, Lingjuan; Yan, Bo; Du, Junping; Shi, Chuan is a scholarly article available to read on EtoBox.

What is Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective about?

To preserve user privacy in recommender systems, federated recommendation (FR) based on federated learning (FL) emerges, keeping the personal data on the local client and updating a model collaboratively. Unlike FL, FR has a unique sparse aggregation mechanism, where the embedding of each item is updated by only partial clients, instead of full clients in a dense aggregation of general FL. Recently, as an essential principle of FL, model security has received increasing attention, especially for Byzantine attacks, where malicious clients can send arbitrary updates. The problem of exploring the Byzantine robustness of FR is particularly critical since in the domains applying FR, e.g., e-commerce, malicious clients can be injected easily by registering new accounts. However, existing Byzantine works neglect the unique sparse aggregation of FR, making them unsuitable for our problem. Thus, we make the first effort to investigate Byzantine attacks on FR from the perspective of sparse aggregation, which is non-trivial: it is not clear how to define Byzantine robustness under sparse aggregations and design Byzantine attacks under limited knowledge/capability. In this paper, we reformulat

Author

Zhang, Zhongjian; Zhang, Mengmei; Wang, Xiao; Lyu, Lingjuan; Yan, Bo; Du, Junping; Shi, Chuan

Published

2025

Language

EN