Federated Learning (FL) is a type of distributed machine learning, which avoids sharing
privacy and sensitive data with a central server. Despite the advances in FL, current …

Federated Learning (FL) has achieved significant achievements recently, enabling
collaborative model training on distributed data over edge devices. Iterative gradient or …

Federated learning (FL) achieves great popularity in broad areas as a powerful interface to
offer intelligent services to customers while maintaining data privacy. Nevertheless, FL faces …

Federated Learning (FL) is a distributed approach where numerous devices train a shared
global model for Machine Learning (ML) tasks. At every training round, the client devices …

Federated learning (FL) involves training a model over massive distributed devices, while
keeping the training data localized and private. This form of collaborative learning exposes …

Federated learning suffers from a latency bottleneck induced by network stragglers, which
hampers the training efficiency significantly. In addition, due to the heterogeneous data …

In Federated Learning (FL) client devices connected over the internet collaboratively train a
machine learning model without sharing their private data with a central server or with other …

Federated Learning (FL) is a distributed learning paradigm that can coordinate
heterogeneous edge devices to perform model training without sharing private raw data …

Federated Learning (FL) has emerged as a new paradigm for training machine learning
models distributively without sacrificing data security and privacy. Learning models on edge …

Federated learning (FL) involves training a model over massive distributed devices, while
keeping the training data localized. This form of collaborative learning exposes new …