1 citations · 2 across the 6 of their papers we have counts for
6 papers
FlexP-SFT: A Flexible Aggregation-Free Framework for On-Device Personalized Split Federated Fine-Tuning of LLMs
Jiaxiang Geng, Tianjun Yuan, Pengchao Han +3
To fine-tune large language models (LLMs) over private data, federated learning (FL) has emerged as a promising paradigm. However, the prohibitive memory and communication demands…
SemiDFL: A Semi-Supervised Paradigm for Decentralized Federated Learning
Xinyang Liu, Pengchao Han, Xuan Li +1
Decentralized federated learning (DFL) realizes cooperative model training among connected clients without relying on a central server, thereby mitigating communication bottlenecks…
Convergence Analysis of Split Federated Learning on Heterogeneous Data
Pengchao Han, Chao Huang, Geng Tian +2
Split federated learning (SFL) is a recent distributed approach for collaborative model training among multiple clients. In SFL, a global model is typically split into two parts, w…
Federated Learning While Providing Model as a Service: Joint Training and Inference Optimization
Pengchao Han, Shiqiang Wang, Yang Jiao +1
While providing machine learning model as a service to process users' inference requests, online applications can periodically upgrade the model utilizing newly collected data. Fed…
FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning
Pengchao Han, Xingyan Shi, Jianwei Huang
Knowledge distillation (KD) can enable collaborative learning among distributed clients that have different model architectures and do not share their local data and model paramete…
Incentive Mechanism Design for Distributed Ensemble Learning
Chao Huang, Pengchao Han, Jianwei Huang
Distributed ensemble learning (DEL) involves training multiple models at distributed learners, and then combining their predictions to improve performance. Existing related studies…