Zero-Trust Based Robust Federated Learning Against Betrayal Behaviors

Xinran Zhang, Dan Wang, Yifei Zhu, Weilong Chen, Zheng Chang, Zhu Han

Abstract: Due to its advantage of protecting data privacy and reducing communication overhead, Federated Learning (FL) is becoming a promising machine learning paradigm. However, resource limitations and unstable communication connections on the participating client end can lead to unintentional failures that degrade FL performance. Moreover, as FL systems scale and interconnect increasingly, they face growing exposure to intentional network risks. Furthermore, the assumption of continued trust in historically benign clients introduces vulnerabilities to potential internal betrayal within FL systems. In this paper, we enhance the robustness of FL by incorporating the zero-trust principle, which eliminates implicit trust in clients and mitigates unintentional failures, intentional attacks, and strategic betrayal risks. The framework incorporates dynamic client selection and aggregation weight allocation through trustworthiness evaluation and sustained skepticism toward each potential betrayal behavior. Specifically, a Dirichlet-based trust evaluation technique is presented to update clients’ trustworthiness with evolving observations. Then, to reduce potential betrayal loss, we formulate a min-max optimization problem that minimizes the worst-case betrayal loss. Next, we transform the formulation into a convex programming problem for solution. Extensive simulations are conducted to demonstrate the efficacy of the zero-trust based FL in the accurate trust assessment and the system’s betrayal-aware robustness enhancement.

External IDs:dblp:journals/tmc/ZhangWZCCH25