Federated Domain Generalization with Data-free On-server Gradient Matching

• URL: http://arxiv.org/abs/2501.14653v1
• Date: Fri, 24 Jan 2025 17:20:22 GMT
• Title: Federated Domain Generalization with Data-free On-server Gradient Matching
• Authors: Trong-Binh Nguyen, Minh-Duong Nguyen, Jinsun Park, Quoc-Viet Pham, Won Joo Hwang,
• Abstract summary: Domain Generalization (DG) aims to learn from multiple known source domains a model that can generalize well to unknown target domains. In this paper, we introduce a novel approach, dubbed Federated Learning via On-server Matching Gradient (FedOMG), which can emphefficiently leverage domain information from distributed domains.
• Score: 6.817783565501387
• License:
• Abstract: Domain Generalization (DG) aims to learn from multiple known source domains a model that can generalize well to unknown target domains. One of the key approaches in DG is training an encoder which generates domain-invariant representations. However, this approach is not applicable in Federated Domain Generalization (FDG), where data from various domains are distributed across different clients. In this paper, we introduce a novel approach, dubbed Federated Learning via On-server Matching Gradient (FedOMG), which can \emph{efficiently leverage domain information from distributed domains}. Specifically, we utilize the local gradients as information about the distributed models to find an invariant gradient direction across all domains through gradient inner product maximization. The advantages are two-fold: 1) FedOMG can aggregate the characteristics of distributed models on the centralized server without incurring any additional communication cost, and 2) FedOMG is orthogonal to many existing FL/FDG methods, allowing for additional performance improvements by being seamlessly integrated with them. Extensive experimental evaluations on various settings to demonstrate the robustness of FedOMG compared to other FL/FDG baselines. Our method outperforms recent SOTA baselines on four FL benchmark datasets (MNIST, EMNIST, CIFAR-10, and CIFAR-100), and three FDG benchmark datasets (PACS, VLCS, and OfficeHome).

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