The ultra-low latency requirements of 5G/6G applications and privacy constraints call for
distributed machine learning systems to be deployed at the edge. With its simple yet …

Federated Learning is a much-needed technology in this golden era of big data and Artificial
Intelligence, due to its vital role in preserving data privacy, and eliminating the need to …

This paper studies the algorithmic stability and generalizability of decentralized stochastic
gradient descent (D-SGD). We prove that the consensus model learned by D-SGD is …

Abstract Hierarchical SGD (H-SGD) has emerged as a new distributed SGD algorithm for
multi-level communication networks. In H-SGD, before each global aggregation, workers …

In federated learning (FL), clients may have diverse objectives, and merging all clients'
knowledge into one global model will cause negative transfer to local performance. Thus …

Federated learning (FL) is an effective approach to train models collaboratively among
distributed edge nodes (ie, workers) while facing three crucial challenges, edge …

In this paper, we propose a robust communication-efficient decentralized learning algorithm,
named RCEDL, to address data heterogeneity, communication heterogeneity and …

Federated and decentralized machine learning leverage end-user devices for privacy-
preserving training of models at lower operating costs than within a data center. In a round of …

Federated learning has been widely applied in autonomous driving since it enables training
a learning model among vehicles without sharing users' data. However, data from …

In practical realizations of a Federated Learning ecosystems, the parties cooperating during
the training process, and that later use the trained/global model may consist of competing …