3 papers
cs.IT2026
FedLoDrop: Federated LoRA with Dropout for Generalized LLM Fine-tuning
Sijing Xie, Dingzhu Wen, Changsheng You +3
Fine-tuning (FT) large language models (LLMs) is crucial for adapting general-purpose models to specific tasks, enhancing accuracy and relevance with minimal resources. To further…
cs.LG2025
Communication Efficient Cooperative Edge AI via Event-Triggered Computation Offloading
You Zhou, Changsheng You, Kaibin Huang
Rare events, despite their infrequency, often carry critical information and require immediate attentions in mission-critical applications such as autonomous driving, healthcare, a…
cs.LG2024
Federated Dropout: Convergence Analysis and Resource Allocation
Sijing Xie, Dingzhu Wen, Xiaonan Liu +3
Federated Dropout is an efficient technique to overcome both communication and computation bottlenecks for deploying federated learning at the network edge. In each training round,…