← 研踪 PaperTrailarXiv:2611.01498 · v1
FedUDG: Federated Domain Generalization with Unlabeled Clients
首次提交 2026-11-01 11:30
版本更新 2026-11-01 11:30
雷达观测 2026-11-04 23:43
摘要
Federated domain generalization (FDG) aims to learn models that generalize reliably to out-of-distribution (OOD) environments without centralizing client's data. However, existing FDG methods assume that all source clients have labeled data, limiting their ability to leverage the abundant unlabeled client data available in practice. To address this limitation, we propose FedUDG. At the federation level, FedUDG utilizes a parameter-functional differentiation strategy that maintains a semantic anchor updated solely using ground-truth labels. The remaining parameters absorb knowledge from unlabeled domains under bounded aggregation, preventing domain-biased noise from eroding the class semantics. Within each unlabeled client, FedUDG utilizes a student-teacher framework with two complementary objectives: (1) semantic self-training on high-confidence samples, using the functional differentiation between the semantic anchor and classifier to constrain the model to learn semantically pure and cross-domain stable features; and (2) label-free representation distillation on all unlabeled samples to stabilize learning under appearance variations. Together, these objectives guide the model toward a transferable feature space, while mitigating overfitting to domain-specific characteristics. Furthermore, to address the lack of systematic evaluation of FDG under formal privacy constraints, we develop a sample-level differentially private extension, DP-FedUDG, to quantify the trade-off between privacy and OOD generalization. Across four evaluation protocols, FedUDG consistently outperforms state-of-the-art baselines. The code is available at https://github.com/Jr10426/FedUDG
作者备注：12 pages, 4 figures
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v1 · 2026-11-01 11:30 · 最新观测版本
FedUDG: Federated Domain Generalization with Unlabeled Clients
Federated domain generalization (FDG) aims to learn models that generalize reliably to out-of-distribution (OOD) environments without centralizing client's data. However, existing FDG methods assume that all source clients have labeled data, limiting their ability to leverage the abundant unlabeled client data available in practice. To address this limitation, we propose FedUDG. At the federation level, FedUDG utilizes a parameter-functional differentiation strategy that maintains a semantic anchor updated solely using ground-truth labels. The remaining parameters absorb knowledge from unlabeled domains under bounded aggregation, preventing domain-biased noise from eroding the class semantics. Within each unlabeled client, FedUDG utilizes a student-teacher framework with two complementary objectives: (1) semantic self-training on high-confidence samples, using the functional differentiation between the semantic anchor and classifier to constrain the model to learn semantically pure and cross-domain stable features; and (2) label-free representation distillation on all unlabeled samples to stabilize learning under appearance variations. Together, these objectives guide the model toward a transferable feature space, while mitigating overfitting to domain-specific characteristics. Furthermore, to address the lack of systematic evaluation of FDG under formal privacy constraints, we develop a sample-level differentially private extension, DP-FedUDG, to quantify the trade-off between privacy and OOD generalization. Across four evaluation protocols, FedUDG consistently outperforms state-of-the-art baselines. The code is available at https://github.com/Jr10426/FedUDG
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