Federated learning (FL) has emerged as a powerful machine learning technique that
enables the development of models from decentralized data sources. However, the …

Recently, federated learning (FL) has achieved wide successes for diverse privacy-sensitive
applications without sacrificing the sensitive private information of clients. However, the data …

Federated Learning (FL) has emerged as a transformative paradigm in machine learning,
enabling decentralized model training across multiple devices while preserving data …

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 noisy label learning (FNLL) is emerging as a promising tool for privacy-
preserving multi-source decentralized learning. Existing research, relying on the assumption …

The increasing demand for intelligent services and privacy protection of mobile and Internet
of Things (IoT) devices motivates the wide application of Federated Edge Learning (FEL), in …

Federated learning (FL) has emerged as a new paradigm for privacy-preserving
collaborative training. Under domain skew the current FL approaches are biased and face …

Training data in federated learning (FL) frameworks can have label noise, since they must
be stored and annotated on clients' devices. If trained over such corrupted data, the models …

Federated learning (FL) has emerged as a promising paradigm for training segmentation
models on decentralized medical data, owing to its privacy-preserving property. However …

Federated learning (FL) aims to collaboratively train a shared model across multiple clients
without transmitting their local data. Data heterogeneity is a critical challenge in realistic FL …