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
learn a powerful global model by aggregating local training. However, the performance of …

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 …

Classical federated learning approaches incur significant performance degradation in the
presence of non-independent and identically distributed (non-IID) client data. A possible …

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

We propose a novel contrastive learning framework to effectively address the challenges of
data heterogeneity in federated learning. We first analyze the inconsistency of gradient …

Client-wise data heterogeneity is one of the major issues that hinder effective training in
federated learning (FL). Since the data distribution on each client may vary dramatically, the …

A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the
generalization of the global model on each client. To address this, we propose a method …

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 …