7 citations · 7 across the 5 of their papers we have counts for
5 papers
ReLoRA: Knowledge-Reusing Adaptation for Fast Rollout of Evolving LLM Services
Yang Xu, Zihuai Xu, Hongli Xu +3
Large Language Models (LLMs) are increasingly deployed as continuously evolving services, where frequent base-model updates may invalidate previously deployed task-specific Low-Ran…
Heroes: Lightweight Federated Learning with Neural Composition and Adaptive Local Update in Heterogeneous Edge Networks
Jiaming Yan, Jianchun Liu, Shilong Wang +3
Federated Learning (FL) enables distributed clients to collaboratively train models without exposing their private data. However, it is difficult to implement efficient FL due to l…
MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation
Yunming Liao, Yang Xu, Hongli Xu +3
Recently, federated learning (FL) has emerged as a popular technique for edge AI to mine valuable knowledge in edge computing (EC) systems. To mitigate the computing/communication…
SemiSFL: Split Federated Learning on Unlabeled and Non-IID Data
Yang Xu, Yunming Liao, Hongli Xu +3
Federated Learning (FL) has emerged to allow multiple clients to collaboratively train machine learning models on their private data at the network edge. However, training and depl…
Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learning
Zhida Jiang, Yang Xu, Hongli Xu +2
Federated learning (FL) allows multiple clients cooperatively train models without disclosing local data. However, the existing works fail to address all these practical concerns i…