Federated learning (FL) is a machine learning paradigm where multiple client devices train
models collaboratively without data exchange. Data heterogeneity problem is naturally …

Federated learning (FL) enables multiple clients to train models collaboratively without
sharing local data, which has achieved promising results in different areas, including the …

Federated Learning (FL) allows training machine learning models in privacy-constrained
scenarios by enabling the cooperation of edge devices without requiring local data sharing …

Federated Learning (FL) has emerged as the state-of-the-art approach for learning from
decentralized data in privacy-constrained scenarios. However, system and statistical …

Federated learning (FL) is a distributed learning framework where numerous clients
collaborate with a central server to train a model without sharing local data. However, the …