One main challenge in federated learning is the large communication cost of exchanging
weight updates from clients to the server at each round. While prior work has made great …

Recently, federated learning (FL) as a new learning paradigm allows multi-party to
collaboratively train a shared global model with privacy protection. However, vanilla FL …

Federated learning has emerged as a promising approach for collaborative model training
across distributed devices. Federated learning faces challenges such as Non-Independent …

The emerging Personalized Federated Learning (PFL) methods aim to produce
personalized models for different users, so as to keep track of their individualized …

The recent development of Foundation Models (FMs), represented by large language
models, vision transformers, and multimodal models, has been making a significant impact …

Federated learning (FL) can train collaboratively with several mobile terminals (MTs), which
faces critical challenges in communication, resource, and privacy. Existing privacy …

In federated learning (FL), accommodating clients' varied computational capacities poses a
challenge, often limiting the participation of those with constrained resources in global …

Federated learning (FL) is a widely used distributed learning framework. However,
constrained wireless environment and intrinsically heterogeneous data across devices can …

Most Federated Learning (FL) algorithms proposed to date obtain the global model by
aggregating multiple local models that typically share the same architecture, thus …

Mobile devices are indispensable sources of big data. Federated learning (FL) has a great
potential in exploiting these private data by exchanging locally trained models instead of …