6 papers
Rethinking Factor Sharing in Federated LoRA: A Rank-Aware Adaptive Approach
Xinyi Xu, Bingnan Xiao, Shuang Qin +2
Low-rank adaptation (LoRA) represents large language model (LLM) updates with two compact matrix factors, i.e., and , providing an efficient way to fine-tune large models in…
Divergence-Based Adaptive Aggregation for Byzantine Robust Federated Learning
Bingnan Xiao, Feng Zhu, Jingjing Zhang +2
Inherent client drifts caused by data heterogeneity, as well as vulnerability to Byzantine attacks within the system, hinder effective model training and convergence in federated l…
Explainable AI for Next-Generation Wireless Physical Layer: Basics, State-of-the-Art, and Open Challenges
Bingnan Xiao, Shuyan Hu, Xiaojing Chen +5
Next-generation wireless systems are expected to be ``AI-native," with neural networks (NNs) embedded throughout the physical (PHY) layer protocol stack to improve spectral efficie…
Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systems
Bingnan Xiao, Chenhao Yang, Wei Ni +2
Network operators' changing policies, service requirements, and stringent real-time constraints render existing methods designed with fixed objectives and constraints ineffective.…
FedVSSAM: Mitigating Flatness Incompatibility in Sharpness-Aware Federated Learning
Bingnan Xiao, Yuan Gao, Bingcong Li +3
Sharpness-aware minimization (SAM) is an effective method for improving the generalization of federated learning (FL) by steering local training toward flat minima. Under data hete…
Adaptive Test-Time Compute Allocation for Reasoning LLMs via Constrained Policy Optimization
Zhiyuan Zhai, Bingcong Li, Bingnan Xiao +2
Test-time compute scaling, the practice of spending extra computation during inference via repeated sampling, search, or extended reasoning, has become a powerful lever for improvi…