Jun Xia

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., …

Enabling On-Device Large Language Model Personalization with Self-Supervised Data Selection and Synthesis

2024

After a large language model (LLM) is deployed on edge devices, it is desirable for these devices to learn from user-generated conversation data to generate user-specific and personalized responses in real-time. However, user-generated data usually …