← Search

Eric Lin

2 accepted papers

2023

Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Federated Object Detection

NeurIPS 2023poster

Federated Learning (FL) has emerged as a potent framework for training models across distributed data sources while maintaining data privacy. Nevertheless, it faces challenges with limited high-quality labels and non-IID client data, particularly in applications like autonomous driving. To address t…

2022

FedLTN: Federated Learning for Sparse and Personalized Lottery Ticket Networks

ECCV 2022poster

"Federated learning (FL) enables clients to collaboratively train a model, while keeping their local training data decentralized. However, high communication costs, data heterogeneity across clients, and lack of personalization techniques hinder the development of FL. In this paper, we propose FedLT…

Cited by 16SourcePDFScholar