Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

Out-of-distribution generalization is challenging for non-participating clients of federated
learning under distribution shifts. A proven strategy is to explore those invariant relationships …

Federated learning (FL) demonstrates its advantages in integrating distributed infrastructure,
communication, computing and learning in a privacy-preserving manner. However, the …

Apart from the promising potential, Federated Learning (FL) faces challenges such as high
communication costs and client heterogeneity. Although numerous works have been …

Due to the unique advantages of collaborative learning on isolated yet unlabeled data,
federated self-supervised learning has received increasing attention from both academic …

Federated reinforcement learning (FedRL) enables agents to collaboratively train a global
policy without sharing their individual data. However, high communication overhead …

Federated learning (FL) is a distributed machine learning paradigm that enables edge
devices to participate in training as clients, and at the same time protect their privacy. Recent …

This paper tackles a challenging decentralized consensus optimization problem defined
over a network of interconnected devices. The devices work collaboratively to solve a …

There is a growing interest in the distributed optimization framework that goes under the
name of Federated Learning (FL). In particular, much attention is being turned to FL …

Federated learning is a commonly distributed framework for large-scale learning, where a
model is learned over massively distributed remote devices without sharing information on …