This chapter deals with decentralized learning algorithms for in-network processing of graph-
valued data. A generic learning problem is formulated and recast into a separable form …

Traditional federated learning algorithms impose strict requirements on the participation
rates of devices, which limit the potential reach of federated learning. This paper extends the …

We study federated machine learning at the wireless network edge, where limited power
wireless devices, each with its own dataset, build a joint model with the help of a remote …

Recently, federated learning (FL) techniques have enabled multiple users to train machine
learning models collaboratively without data sharing. However, existing FL algorithms suffer …

In this article, the problem of training federated learning (FL) algorithms over a realistic
wireless network is studied. In the considered model, wireless users execute an FL …

Federated learning (FL) is an attractive paradigm for making use of rich distributed data
while protecting data privacy. Nonetheless, non-ideal communication links and limited …