Redundancy in cost functions for Byzantine fault-tolerant federated learning

Federated learning has gained significant attention in recent years owing to the development of hardware and rapid growth in data collection. However, its ability to incorporate a large number of participating agents with various data sources makes federated learning susceptible to adversarial agents. This paper summarizes our recent results on server-based Byzantine fault-tolerant distributed optimization with applicability to resilience in federated learning. Specifically, we characterize redundancies in agents' cost functions that are necessary and sufficient for provable Byzantine resilience in distributed optimization. We discuss the implications of these results in the context of federated learning.