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Shen, Z., Cui, P., Kuang, K., Li, B. and Chen, P. (2018) Causally Regularized Learning with Agnostic Data Selection Bias. Proceedings of the 26th ACM international conference on Multimedia, Seoul, 22-26 October 2018, 411-419.
https://doi.org/10.1145/3240508.3240577

has been cited by the following article:

• TITLE: Federated Domain Generalization by Intra-Client Stable Feature Learning and Inter-Client Domain Adversarial Alignment

  AUTHORS: Xin Zhao, Wu Ai

  KEYWORDS: Federated Domain Generalization, Stable Feature Learning, Domain Adversarial Alignment, Agnostic Distribution Bias

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.15 No.4, April 15, 2025

  ABSTRACT: Federated Learning (FL), which serves as a distributed learning model focused on privacy protection, has received widespread attention due to its ability to collaboratively train models without exchanging clients’ raw data. However, since each client independently collects its local dataset, a single client may contain data from multiple domains, which leads to the existence of an agnostic distribution bias both intra-client and inter-client biases, and it is still a challenge to implement cross-domain generalization of the model in such an FL setting. In this paper, we propose a new stable federated adversarial domain generalization (Stable-FedADG) algorithm, which considers both intra-client and inter-client multi-domain distributions and through intra-client stable feature learning and inter-client domain adversarial alignment to learn domain-invariant features in federated scenarios. Experiments verify that the Stable-FedADG algorithm enhances the overall model performance across various federated environments.