Federated learning (FL) has prevailed as an efficient and privacy-preserved scheme for
distributed learning. In this work, we mainly focus on the optimization of computation and …

Federated learning (FL) offers a solution to train a global machine learning model while still
maintaining data privacy, without needing access to data stored locally at the clients …

In recent years, federated learning (FL) plays an important role in data privacy-sensitive
scenarios to perform learning works collectively without data exchange. However, due to the …

Federated Learning (FL) has attracted significant attention as an efficient privacy-preserving
approach to distributed learning across multiple clients. Despite extensive empirical …

Federated Learning (FL) emerges as a new learning paradigm that enables multiple devices
to collaboratively train a shared model while preserving data privacy. However, intensive …

Federated learning (FL) enables distributed optimization of machine learning models while
protecting privacy by independently training local models on each client and then …

Federated learning (FL) has become de facto framework for collaborative learning among
edge devices with privacy concern. The core of the FL strategy is the use of stochastic …

Federated learning (FL) is a distributed neural network training paradigm with privacy
protection. With the premise of ensuring that local data isn't leaked, multi-device cooperation …

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 a new paradigm for training machine learning
models distributively without sacrificing data security and privacy. Learning models on edge …