Abstract
Federated Learning (FL) can be coordinated under the orchestration of a central server to build a privacy-preserving model without collaborative data exchange. However, participant data heterogeneity leads to local optima divergence, affecting convergence outcomes. Recent research focused on global sharpness-aware minimization (SAM) and dynamic regularization to enhance consistency between global and local generalization and optimization objectives in FL. Nonetheless, the estimation of global SAM introduces additional computational and memory overhead. At the same time, the local dynamic regularizer cannot capture the global update state due to training isolation. This paper proposes a novel FL algorithm, FedTOGA, designed to consider optimization and generalization objectives while maintaining minimal uplink communication overhead. By linking local perturbations to global updates, we improve global generalization consistency. Additionally, by linking the dynamic regularizer to global updates, FedTOGA improves global gradient perception and strengthens optimization consistency. Crucially, global updates are directly delivered to clients, allowing them to incorporate global knowledge without communication and computational cost. We also propose neighborhood perturbation to enhance local perturbation, analyzing its strengths and working principles. Theoretical analysis shows FedTOGA achieves faster convergence O(1/T) on the non-convex function. Empirical studies demonstrate that FedTOGA outperforms existing algorithms, with a 1% accuracy increase and 30% faster convergence, achieving SOTA .