Federated Learning (FL) has gained considerable attention recently, as it allows clients to
cooperatively train a global machine learning model without sharing raw data. However, its …

Federated Learning (FL) has emerged as a privacy-preserving paradigm enabling
collaborative model training among distributed clients. However, current FL methods …

Federated learning (FL) is an emerging distributed machine learning (ML) paradigm with
enhanced privacy, aiming to achieve a" good" ML model for as many as participants while …

Federated Learning (FL) enables collaborative training of machine learning models on
decentralized data while preserving data privacy. However, data across clients often differs …

Federated learning (FL) facilitates collaboration between a group of clients who seek to train
a common machine learning model without directly sharing their local data. Although there …

In mobile and IoT systems, Federated Learning (FL) is increasingly important for effectively
using data while maintaining user privacy. One key challenge in FL is managing statistical …

Federated Learning (FL) enables multiple clients to collaboratively learn in a distributed
way, allowing for privacy protection. However, the real-world non-IID data will lead to client …

Federated learning (FL) is a promising approach that enables distributed clients to
collaboratively train a global model while preserving their data privacy. However, FL often …

Federated Learning (FL) is a strategy for training distributed learning models. This approach
gives rise to significant challenges including the non-independent and identically distributed …

Federated learning (FL) has emerged as a promising paradigm that enables clients to
collaboratively train a shared global model without uploading their local data. To alleviate …