FedMuon: Accelerating Federated Learning with Matrix Orthogonalization
PositiveArtificial Intelligence
A new study introduces FedMuon, a method designed to enhance Federated Learning (FL) by addressing its communication bottlenecks. By utilizing matrix orthogonalization, FedMuon aims to improve the effectiveness of local updates, which is crucial for reducing the number of communication rounds. This advancement is significant as it could lead to more efficient FL processes, ultimately benefiting various applications that rely on decentralized data processing.
— Curated by the World Pulse Now AI Editorial System



