CovFedAvg: Local Low-Loss Curves and Federated Model Selection
arXiv:2610.39902v1 Announce Type: cross Abstract: Clients observing different parts of a prediction task may learn locally equivalent models with incompatible behavior outside their observed support. We study whether redundant models permit local low-loss paths toward a compatible shared model. CovFedAvg retains local models and curve controls on clients, fits a common endpoint through aggregated gradient contributions, and selects an actual candidate model using distributed training losses. No…