Communication-Efficient Distributed Deep Learning via Federated Dynamic Averaging

Michail Theologitis, Georgios Frangias, Georgios Anestis +2

The ever-growing volume and decentralized nature of data, coupled with the need to harness it and extract knowledge, have led to the extensive use of distributed deep learning (DDL…