Related papers: Federated Learning with Domain Generalization

Federated Learning with Domain Generalization

URL: http://arxiv.org/abs/2111.10487v1
Date: Sat, 20 Nov 2021 01:02:36 GMT
Title: Federated Learning with Domain Generalization
Authors: Liling Zhang, Xinyu Lei, Yichun Shi, Hongyu Huang and Chao Chen
Abstract summary: Federated Learning enables a group of clients to jointly train a machine learning model with the help of a centralized server. In practice, the model trained over multiple source domains may have poor generalization performance on unseen target domains. We propose FedADG to equip federated learning with domain generalization capability.
Score: 11.92860245410696
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Federated Learning (FL) enables a group of clients to jointly train a machine learning model with the help of a centralized server. Clients do not need to submit their local data to the server during training, and hence the local training data of clients is protected. In FL, distributed clients collect their local data independently, so the dataset of each client may naturally form a distinct source domain. In practice, the model trained over multiple source domains may have poor generalization performance on unseen target domains. To address this issue, we propose FedADG to equip federated learning with domain generalization capability. FedADG employs the federated adversarial learning approach to measure and align the distributions among different source domains via matching each distribution to a reference distribution. The reference distribution is adaptively generated (by accommodating all source domains) to minimize the domain shift distance during alignment. In FedADG, the alignment is fine-grained since each class is aligned independently. In this way, the learned feature representation is supposed to be universal, so it can generalize well on the unseen domains. Extensive experiments on various datasets demonstrate that FedADG has better performance than most of the previous solutions even if they have an additional advantage that allows centralized data access. To support study reproducibility, the project codes are available in https://github.com/wzml/FedADG