Federated Learning (FL) enables joint training across distributed clients using their local
data privately. Federated Multi-Task Learning (FMTL) builds on FL to handle multiple tasks …

Federated Learning (FL) enables joint training across distributed clients using their local
data privately. Federated Multi-Task Learning (FMTL) builds on FL to handle multiple tasks …

Federated Learning (FL) was originally proposed to effectively exploit more data that are
distributed at local clients even though the local data follow non-iid distributions. The …

Federated learning (FL) is a distributed learning paradigm that enables multiple clients to
collaboratively learn a shared global model. Despite the recent progress, it remains …

Federated Learning (FL) enables edge devices or clients to collaboratively train machine
learning (ML) models without sharing their private data. Much of the existing work in FL …

Abstract Personalized Federated Learning (PFL) is primarily designed to provide
customized models for each client to better fit the non-iid distributed client data which is a …

Federated Learning (FL) systems target distributed model training on decentralized and
private local training data belonging to users. Most of the existing methods aggregate …

Federated learning (FL) is an attractive distributed learning paradigm supporting real-time
continuous learning and client privacy by default. In most FL approaches, all edge clients …

Federated learning (FL) allows multiple clients to collectively train a high-performance
global model without sharing their private data. However, the key challenge in federated …

Federated Learning (FL) is an emerging machine learning framework that enables multiple
clients (coordinated by a server) to collaboratively train a global model by aggregating the …