FedSEAL: Semi-Supervised Federated Learning with Self-Ensemble Learning and Negative Learning

• URL: http://arxiv.org/abs/2110.07829v1
• Date: Fri, 15 Oct 2021 03:03:23 GMT
• Title: FedSEAL: Semi-Supervised Federated Learning with Self-Ensemble Learning and Negative Learning
• Authors: Jieming Bian, Zhu Fu, Jie Xu
• Abstract summary: Federated learning (FL) is a popular decentralized and privacy-preserving machine learning (FL) framework. In this paper, we propose a new FL algorithm, called FedSEAL, to solve this Semi-Supervised Federated Learning (SSFL) problem. Our algorithm utilizes self-ensemble learning and complementary negative learning to enhance both the accuracy and the efficiency of clients' unsupervised learning on unlabeled data.
• Score: 7.771967424619346
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated learning (FL), a popular decentralized and privacy-preserving machine learning (FL) framework, has received extensive research attention in recent years. The majority of existing works focus on supervised learning (SL) problems where it is assumed that clients carry labeled datasets while the server has no data. However, in realistic scenarios, clients are often unable to label their data due to the lack of expertise and motivation while the server may host a small amount of labeled data. How to reasonably utilize the server labeled data and the clients' unlabeled data is thus of paramount practical importance. In this paper, we propose a new FL algorithm, called FedSEAL, to solve this Semi-Supervised Federated Learning (SSFL) problem. Our algorithm utilizes self-ensemble learning and complementary negative learning to enhance both the accuracy and the efficiency of clients' unsupervised learning on unlabeled data, and orchestrates the model training on both the server side and the clients' side. Our experimental results on Fashion-MNIST and CIFAR10 datasets in the SSFL setting validate the effectiveness of our method, which outperforms the state-of-the-art SSFL methods by a large margin.

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