Taming Client Availability in Federated Learning in the Presence of Arbitrary and Unknown Dynamics

Federated learning (FL) is a prominent distributed learning framework that allows clients to train machine learning models orchestrated by a parameter server (PS) [3]. Unfortunately, its practical implementation is fundamentally hindered by heterogeneous local data distribution and intermittent client availability [5]. Moreover, the availability of a client may be highly dynamic and hard to predict.