3 papers
cs.LG2026
FedAdaVR: Adaptive Variance Reduction for Robust Federated Learning under Limited Client Participation
S M Ruhul Kabir Howlader, Xiao Chen, Yifei Xie +1
Federated learning (FL) encounters substantial challenges due to heterogeneity, leading to gradient noise, client drift, and partial client participation errors, the last of which…
cs.AI2026
Multi-Agent Collaborative Reward Design for Enhancing Reasoning in Reinforcement Learning
Pei Yang, Ke Zhang, Ji Wang +5
We present CRM (Multi-Agent Collaborative Reward Model), a framework that replaces a single black-box reward model with a coordinated team of specialist evaluators to improve robus…
cs.LG2025
FedOAED: Federated On-Device Autoencoder Denoiser for Heterogeneous Data under Limited Client Availability
S M Ruhul Kabir Howlader, Xiao Chen, Yifei Xie +1
Over the last few decades, machine learning (ML) and deep learning (DL) solutions have demonstrated their potential across many applications by leveraging large amounts of high-qua…