Byzantine-Robust Federated Learning with Learnable Aggregation Weights
This addresses the robustness challenge in federated learning for applications like healthcare or finance where data privacy and security are critical, representing a novel method rather than an incremental improvement.
The paper tackles the problem of Byzantine-robust federated learning under heterogeneous data distributions by proposing a novel optimization formulation with learnable aggregation weights, and it demonstrates consistent performance improvements over state-of-the-art methods, especially with highly heterogeneous data and many malicious clients.
Federated Learning (FL) enables clients to collaboratively train a global model without sharing their private data. However, the presence of malicious (Byzantine) clients poses significant challenges to the robustness of FL, particularly when data distributions across clients are heterogeneous. In this paper, we propose a novel Byzantine-robust FL optimization problem that incorporates adaptive weighting into the aggregation process. Unlike conventional approaches, our formulation treats aggregation weights as learnable parameters, jointly optimizing them alongside the global model parameters. To solve this optimization problem, we develop an alternating minimization algorithm with strong convergence guarantees under adversarial attack. We analyze the Byzantine resilience of the proposed objective. We evaluate the performance of our algorithm against state-of-the-art Byzantine-robust FL approaches across various datasets and attack scenarios. Experimental results demonstrate that our method consistently outperforms existing approaches, particularly in settings with highly heterogeneous data and a large proportion of malicious clients.