Artificial Intelligence (AI) has demonstrated significant potential in automating various
medical imaging tasks, which could soon become routine in clinical practice for disease …

Data heterogeneity is an inherent challenge that hinders the performance of federated
learning (FL). Recent studies have identified the biased classifiers of local models as the key …

Federated learning (FL) has emerged as a secure paradigm for collaborative training among
clients. Without data centralization, FL allows clients to share local information in a privacy …

Quantum Federated Learning (QFL) recently becomes a promising approach with the
potential to revolutionize Machine Learning (ML). It merges the established strengths of …

One of the key challenges in federated learning (FL) is local data distribution heterogeneity
across clients, which may cause inconsistent feature spaces across clients. To address this …

Federated Learning (FL) enhances predictive accuracy in load forecasting by integrating
data from distributed load networks while ensuring data privacy. However, the …

Federated learning (FL) has recently emerged as a popular privacy-preserving collaborative
learning paradigm. However, it suffers from the non-independent and identically distributed …

Federated learning (FL) is a distributed learning paradigm that maximizes the potential of
data-driven models for edge devices without sharing their raw data. However, devices often …

Federated Learning (FL) is a distributed machine learning approach that protects user
privacy by collaboratively training shared models across devices without sharing their raw …

Federated Learning (FL) has recently emerged as a novel machine learning paradigm
allowing to preserve privacy and to account for the distributed nature of the learning process …