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 …

Federated Learning (FL) is a distributed machine learning approach in which clients
contribute to learning a global model in a privacy preserved manner. Effective aggregation …

Federated Learning (FL) proposed in recent years has received significant attention from
researchers in that it can enable multiple clients to cooperatively train global models without …

Federated Learning allows multiple participants to jointly train a global model while
guaranteeing the confidentiality and integrity of private datasets. However, current server …

Federated learning (FL) has been widely acknowledged as a promising solution to training
machine learning (ML) model training with privacy preservation. To reduce the traffic …

Federated learning (FL) is a kind of distributed machine learning framework, where the
global model is generated on the centralized aggregation server based on the parameters of …

Federated learning (FL) has emerged as a promising approach for collaborative model
training across decentralized devices while maintaining data privacy. This paper provides a …

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 …

Due to the fast advancement of artificial intelligence (AI), centralized-based models have
become critical for healthcare tasks like in medical image analysis and human behavior …

Federated Learning (FL) is emerging as a promising technology to build machine learning
models in a decentralized, privacy-preserving fashion. Indeed, FL enables local training on …