Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization. (arXiv:2201.07912v1 [cs.LG])

Federated learning (FL) is a useful tool in distributed machine learning that
utilizes users' local datasets in a privacy-preserving manner. When deploying
FL in a constrained wireless environment; however, training models in a
time-efficient manner can be a challenging task due to intermittent
connectivity of devices, heterogeneous connection quality, and non-i.i.d. data.
In this paper, we provide a novel convergence analysis of non-convex loss
functions using FL on both i.i.d. and non-i.i.d. datasets with arbitrary device
selection probabilities for each …

arxiv communication federated learning learning optimization scheduling stochastic