@inproceedings{f56ffe8c430f40dc86611aa727ada3de,
title = "Federated Long-Tailed Learning by Retraining the Biased Classifier with Prototypes",
abstract = "Federated learning is a privacy-preserving framework that collaboratively trains the global model without sharing raw data among clients. However, one significant issue encountered in federated learning is that biased classifiers affect the classification performance of the global model, especially when training on long-tailed data. Retraining the classifier on balanced datasets requires sharing the client{\textquoteright}s information and poses the risk of privacy leakage. We propose a method for retraining the biased classifier using prototypes, that leverage the comparison of distances between local and global prototypes to guide the local training process. We conduct experiments on CIFAR-10-LT and CIFAR-100-LT, and our approach outperforms the accuracy of baseline methods, with accuracy improvements of up to 10%.",
keywords = "Federated learning, Long-tailed data, Privacy protection, Prototype learning",
author = "Yang Li and Kan Li",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.; 6th International Conference on Frontiers in Cyber Security, FCS 2023 ; Conference date: 21-08-2023 Through 23-08-2023",
year = "2024",
doi = "10.1007/978-981-99-9331-4_38",
language = "English",
isbn = "9789819993307",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "575--585",
editor = "Haomiao Yang and Rongxing Lu",
booktitle = "Frontiers in Cyber Security - 6th International Conference, FCS 2023, Revised Selected Papers",
address = "Germany",
}