Mingsong Chen

Have Your Cake and Eat It Too: Toward Efficient and Accurate Split Federated Learning

2023 · arXiv (Cornell University)

Due to its advantages in resource constraint scenarios, Split Federated Learning (SFL) is promising in AIoT systems. However, due to data heterogeneity and stragglers, SFL suffers from the challenges of low inference accuracy and low …

FedEntropy: Efficient Federated Learning for Non-IID Scenarios Using Maximum Entropy Judgment-based Client Selection

2023

Although various techniques have been proposed for Federated Learning (FL) to address its problem of low classification accuracy in non-IID scenarios, most of them neglect both i) distinct data distribution characteristics of heterogeneous devices (i.e., …

CE-FFT: Communication-Efficient Federated Fine-Tuning for Large Language Models via Quantization and In-Context Learning

2025

Although Federated Fine-Tuning (FFT) facilitates the fine-tuning of Large Language Models (LLMs) across data owners without compromising their privacy, it suffers from severe communication overheads caused by numerous parameters of LLMs even with Parameter-Efficient Fine-Tuning …