When workers are heterogeneous in computing and transmission capabilities, the global
efficiency of federated learning suffers from the straggler issue, ie, the slowest worker drags …

Federated learning has emerged as an important distributed learning paradigm, which
normally involves collaborative updating with others and local updating on private data …

Federated learning (FL) has been widely deployed in edge computing scenarios. However,
FL-related technologies are still facing severe challenges while evolving rapidly. Among …

Client-wise data heterogeneity is one of the major issues that hinder effective training in
federated learning (FL). Since the data distribution on each client may vary dramatically, the …

Federated learning is a powerful distributed learning scheme that allows numerous edge
devices to collaboratively train a model without sharing their data. However, training is …

Federated learning (FL) has emerged as a promising distributed learning paradigm that
enables a large number of mobile devices to cooperatively train a model without sharing …

Despite achieving remarkable performance, Federated Learning (FL) encounters two
important problems, ie, low training efficiency and limited computational resources. In this …

Despite achieving remarkable performance, Federated Learning (FL) suffers from two critical
challenges, ie, limited computational resources and low training efficiency. In this paper, we …

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 allows edge devices to collaboratively train a shared model on their
local data without leaking user privacy. The non-independent-and-identically-distributed …