Efficient Adaptive Federated Optimization

Adaptive optimization is critical in federated learning, where enabling adaptivity on both the server and client sides has proven essential for achieving optimal performance. However, the scalability of such jointly adaptive systems is often hindered by resource limitations in communication and memory. In this paper, we introduce a class of efficient adaptive algorithms, named FedAda2 and its enhanced version FedAda2++, designed specifically for large-scale, cross-device federated environments.