FedOptima: Optimizing Resource Utilization in Federated Learning

ArXi:2504.13850v2 Announce Type: replace-cross Federated learning (FL) systems facilitate distributed machine learning across a server and multiple devices. However, FL systems have low resource utilization on servers and devices, limiting their practical use in the real world. This inefficiency primarily arises from two types of idle time: (i) task dependency between the server and devices, and (ii) stragglers among heterogeneous devices. This paper