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.IT2025
Integrated Sensing, Communication, and Computation for Over-the-Air Federated Edge Learning
Dingzhu Wen, Sijing Xie, Xiaowen Cao +4
This paper studies an over-the-air federated edge learning (Air-FEEL) system with integrated sensing, communication, and computation (ISCC), in which one edge server coordinates mu…
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,…