One of the crucial issues in federated learning is how to develop efficient optimization
algorithms. Most of the current ones require full device participation and/or impose strong …

Personalized federated learning considers learning models unique to each client in a
heterogeneous network. The resulting client-specific models have been purported to …

The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the
future, IoFT, the “cloud” will be substituted by the “crowd” where model training is brought to …

Federated Learning (FL) is a distributed learning paradigm that can learn a global or
personalized model from decentralized datasets on edge devices. However, in the computer …

Personalised federated learning (FL) aims at collaboratively learning a machine learning
model tailored for each client. Albeit promising advances have been made in this direction …

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

Abstract Personalized Federated Learning faces many challenges such as expensive
communication costs, training-time adversarial attacks, and performance unfairness across …

Bird's eye view (BEV) perception is becoming increasingly important in the field of
autonomous driving. It uses multi-view camera data to learn a transformer model that directly …

In this work, besides improving prediction accuracy, we study whether personalization could
bring robustness benefits to backdoor attacks. We conduct the first study of backdoor attacks …

This paper advocates a new paradigm Personalized Empirical Risk Minimization (PERM) to
facilitate learning from heterogeneous data sources without imposing stringent constraints …