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cs.LG2025
Diffusion Models for Reinforcement Learning: Foundations, Taxonomy, and Development
Changfu Xu, Jianxiong Guo, Yuzhu Liang +7
Diffusion Models (DMs), as a leading class of generative models, offer key advantages for reinforcement learning (RL), including multi-modal expressiveness, stable training, and tr…
cs.LG2025
FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation
Zihao Peng, Jiandian Zeng, Boyuan Li +3
Federated Learning (FL) facilitates the fine-tuning of Foundation Models (FMs) using distributed data sources, with Low-Rank Adaptation (LoRA) gaining popularity due to its low com…
cs.LG2024
Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks
Changfu Xu, Jianxiong Guo, Wanyu Lin +5
Artificial Intelligence Generated Content (AIGC) has gained significant popularity for creating diverse content. Current AIGC models primarily focus on content quality within a cen…