Evidence-based Model Alignment for Uncoordinated One-Shot Federated Learning

One-Shot Federated Learning is a paradigm in which a global model is learned from data distributed across multiple clients in a single communication round. This setting becomes particularly challenging when the training process is uncoordinated, i.e., with clients starting from different initializations and training for different numbers of epochs. We analyze this problem from a Bayesian perspective, showing how the permutation symmetries of client posteriors can be exploited to improve the resulting global model. Based on these observations, we propose FLAME, a principled approach that identifies client permutations for model alignment by maximizing the Bayesian evidence of the merged model, and then performs Bayesian merging, thus handling permutation symmetries and weight uncertainty in a unified manner. We show on standard benchmarks that our proposal outperforms competing approaches that typically rely either on permutation-based matching or on client loss curvature alone.