4 citations · 17 across the 18 of their papers we have counts for
29 papers
FLoKD: Adaptive Knowledge Distillation for Federated Low-Rank LLM over Wireless Networks
Xinlu Zhang, Na Yan, Yang Su +2
Large language models (LLMs) have demonstrated strong capabilities across a wide range of natural language processing tasks. However, conventional fine-tuning typically relies on c…
TT-Prune: Joint Model Pruning and Resource Allocation for Communication-efficient Time-triggered Federated Learning
Xinlu Zhang, Yansha Deng, Toktam Mahmoodi
Federated learning (FL) offers new opportunities in machine learning, particularly in addressing data privacy concerns. In contrast to conventional event-based federated learning,…
Communication-Aware Knowledge Distillation for Federated LLM Fine-Tuning over Wireless Networks
Xinlu Zhang, Na Yan, Yang Su +2
Federated learning (FL) for large language models (LLMs) offers a privacy-preserving scheme, enabling clients to collaboratively fine-tune locally deployed LLMs or smaller language…
xApp Conflict Mitigation with Scheduler
Idris Cinemre, Toktam Mahmoodi, Amirmohammad Farzaneh
Open RAN (O-RAN) fosters multi-vendor interoperability and data-driven control but simultaneously introduces the challenge of coordinating pre-trained xApps that may produce confli…
UltraFlwr -- An Efficient Federated Surgical Object Detection Framework
Yang Li, Soumya Snigdha Kundu, Maxence Boels +6
Surgical object detection in laparoscopic videos enables real-time instrument identification for workflow analysis and skills assessment, but training robust models such as You Onl…
AI-Native Multi-Access Future Networks -- The REASON Architecture
Konstantinos Katsaros, Ioannis Mavromatis, Kostantinos Antonakoglou +13
The development of the sixth generation of communication networks (6G) has been gaining momentum over the past years, with a target of being introduced by 2030. Several initiatives…