3 papers
cs.LG2026
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…
cs.LG2025
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…
cs.LG2024
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…