Federated learning (FL) is a distributed machine learning (ML) approach that enables
models to be trained on client devices while ensuring the privacy of user data. Model …

Trained on massive publicly available data, large language models (LLMs) have
demonstrated tremendous success across various fields. While more data contributes to …

Recently, personalized federated learning (pFL) has attracted increasing attention in privacy
protection, collaborative learning, and tackling statistical heterogeneity among clients, eg …

Data heterogeneity is an inherent challenge that hinders the performance of federated
learning (FL). Recent studies have identified the biased classifiers of local models as the key …

Prompt learning in pretrained visual-language models has shown remarkable flexibility
across various downstream tasks. Leveraging its inherent lightweight nature recent research …

Partially class-disjoint data (PCDD), a common yet under-explored data formation where
each client contributes a part of classes (instead of all classes) of samples, severely …

One of the key challenges in federated learning (FL) is local data distribution heterogeneity
across clients, which may cause inconsistent feature spaces across clients. To address this …

Abstract In Federated Learning (FL) the issue of statistical data heterogeneity has been a
significant challenge to the field's ongoing development. This problem is further exacerbated …

Federated Learning (FL) has attracted significant attention as an efficient privacy-preserving
approach to distributed learning across multiple clients. Despite extensive empirical …

Federated Learning (FL) is an emerging distributed machine learning paradigm crucial for
ensuring privacy-preserving learning. In FL, a fair incentive mechanism is indispensable for …