Federated learning is promising for its capability to collaboratively train models with multiple
clients without accessing their data, but vulnerable when clients' data distributions diverge …

We address the problem of federated learning (FL) where users are distributed and
partitioned into clusters. This setup captures settings where different groups of users have …

Federated Learning (FL) is an increasingly popular machine learning paradigm in which
multiple nodes try to collaboratively learn under privacy, communication and multiple …

Federated learning is a privacy-preserving distributed learning paradigm where multiple
devices collaboratively train a model, which is applicable to edge computing environments …

Federated learning (FL) is a promising approach to distributed compute, as well as
distributed data, and provides a level of privacy and compliance to legal frameworks. This …

The distinct attributes of Internet of Things (IoT) devices, including the disparity between
training and testing data distributions and limited availability of training data, pose …

Abstract The integration of Foundation Models (FMs) with Federated Learning (FL) presents
a transformative paradigm in Artificial Intelligence (AI), offering enhanced capabilities while …

Federated learning (FL) is a collaborative machine learning approach that enables multiple
clients to train models without sharing their private data. With the rise of deep learning, large …

Personalized federated learning (PFL) is a framework that targets individual models for
optimization, providing better privacy and flexibility for clients. However, in challenging …

We study fully decentralized learning in which agents learn collaborative, yet personalized
prediction models. Specifically, when learners' local datasets are non-IID, a collaboratively …