Byzantine-Robust Federated Learning across Selection Strategies and Partial-Participation Regimes

Federated learning often operates under partial client participation, where only a subset of clients contributes in each communication round. In the presence of Byzantine clients, non-uniform client selection can both bias the observed client population and substantially change the fraction of adversarial clients seen by the server. We propose RaP, a Byzantine-robust federated optimization method for partial participation and general client selection. RaP combines partial-participation bias correction with trial-function-based robust aggregation, validating both fresh updates and the accumulated correction information reused across rounds. The method does not require an honest majority among the clients selected in each round and accommodates stochastic client unavailability. We establish convergence guarantees under heterogeneous client objectives and partial participation. Experiments on heterogeneous CIFAR-10 and real-world ECG classification show that RaP remains stable across uniform and non-uniform client-selection strategies and consistently outperforms Byzantine-robust baselines in challenging adversarial regimes.