OFedQIT: Communication-Efficient Online Federated Learning via Quantization and Intermittent Transmission. (arXiv:2205.06491v1 [cs.LG])

Online federated learning (OFL) is a promising framework to collaboratively
learn a sequence of non-linear functions (or models) from distributed streaming
data incoming to multiple clients while keeping the privacy of their local
data. In this framework, we first construct a vanilla method (named OFedAvg) by
incorporating online gradient descent (OGD) into the de facto aggregation
method (named FedAvg). Despite its optimal asymptotic performance, OFedAvg
suffers from heavy communication overhead and long learning delay. To tackle
these shortcomings, we propose …

arxiv communication federated learning intermittent learning quantization