Federated and continual learning are training paradigms addressing data distribution shift in
space and time. More specifically, federated learning tackles non-iid data in space as …

Federated learning has emerged as an important distributed learning paradigm, which
normally involves collaborative updating with others and local updating on private data …

This paper focuses on an under-explored yet important problem: Federated Class-Continual
Learning (FCCL), where new classes are dynamically added in federated learning. Existing …

Federated Learning (FL) aggregates locally trained models from individual clients to
construct a global model. While FL enables learning a model with data privacy it often …

Abstract Recent developments in Artificial Intelligence techniques have enabled their
successful application across a spectrum of commercial and industrial settings. However …

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 …

Federated learning shows a bright promise as a privacy-preserving collaborative learning
technique. However, prevalent solutions mainly focus on all private data sampled from the …

Although federated learning (FL) has recently been proposed for efficient distributed training
and data privacy protection, it still encounters many obstacles. One of these is the naturally …

In the medical field, multi-center collaborations are often sought to yield more generalizable
findings by leveraging the heterogeneity of patient and clinical data. However, recent privacy …

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 …