7 citations · 7 across the 5 of their papers we have counts for
5 papers · 1 filter
CA-HFP: Curvature-Aware Heterogeneous Federated Pruning with Model Reconstruction
Gang Hu, Yinglei Teng, Pengfei Wu +1
Federated learning on heterogeneous edge devices requires personalized compression while preserving aggregation compatibility and stable convergence. We present Curvature-Aware Het…
FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge
Gang Hu, Yinglei Teng, Pengfei Wu +1
As FMs drive progress toward Artificial General Intelligence (AGI), fine-tuning them under privacy and resource constraints has become increasingly critical particularly when highq…
Split Federated Learning Over Heterogeneous Edge Devices: Algorithm and Optimization
Yunrui Sun, Gang Hu, Yinglei Teng +1
Split Learning (SL) is a promising collaborative machine learning approach, enabling resource-constrained devices to train models without sharing raw data, while reducing computati…
Faster Convergence on Heterogeneous Federated Edge Learning: An Adaptive Clustered Data Sharing Approach
Gang Hu, Yinglei Teng, Nan Wang +1
Federated Edge Learning (FEEL) emerges as a pioneering distributed machine learning paradigm for the 6G Hyper-Connectivity, harnessing data from the Internet of Things (IoT) device…
Clustered Data Sharing for Non-IID Federated Learning over Wireless Networks
Gang Hu, Yinglei Teng, Nan Wang +1
Federated Learning (FL) is a novel distributed machine learning approach to leverage data from Internet of Things (IoT) devices while maintaining data privacy. However, the current…