2 papers
cs.LG2026
Conformalized Neural Networks for Federated Uncertainty Quantification under Dual Heterogeneity
Quang-Huy Nguyen, Jiaqi Wang, Wei-Shinn Ku
Federated learning (FL) faces challenges in uncertainty quantification (UQ). Without reliable UQ, FL systems risk deploying overconfident models at under-resourced agents, leading…
cs.LG2025
Optimized Local Updates in Federated Learning via Reinforcement Learning
Ali Murad, Bo Hui, Wei-Shinn Ku
Federated Learning (FL) is a distributed framework for collaborative model training over large-scale distributed data, enabling higher performance while maintaining client data pri…