4 papers
Flexible Personalized Split Federated Learning for On-Device Fine-Tuning of Foundation Models
Tianjun Yuan, Jiaxiang Geng, Pengchao Han +2
Fine-tuning foundation models is critical for superior performance on personalized downstream tasks, compared to using pre-trained models. Collaborative learning can leverage local…
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…
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…
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…