Co-Boosting++: Coupled Optimization of Data and Ensemble for One-Shot Federated Learning

Summary

Co-Boosting++ is a one-shot federated learning framework that couples synthetic data generation with ensemble construction, so each improves the other across iterations. It fixes the ensemble to generate adversarial hard samples, then uses a Mixture of Experts mechanism to reweight the ensemble based on those samples. The authors report consistent gains over state-of-the-art methods on benchmark datasets and state that it needs no local training modifications, extra transmissions, or client architecture restrictions.