FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866929687783014400 |
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| author | Gupta, Sunny Sutar, Vinay Singh, Varunav Sethi, Amit |
| author_facet | Gupta, Sunny Sutar, Vinay Singh, Varunav Sethi, Amit |
| contents | Federated Learning (FL) offers a decentralized paradigm for collaborative model training without direct data sharing, yet it poses unique challenges for Domain Generalization (DG), including strict privacy constraints, non-i.i.d. local data, and limited domain diversity. We introduce FedAlign, a lightweight, privacy-preserving framework designed to enhance DG in federated settings by simultaneously increasing feature diversity and promoting domain invariance. First, a cross-client feature extension module broadens local domain representations through domain-invariant feature perturbation and selective cross-client feature transfer, allowing each client to safely access a richer domain space. Second, a dual-stage alignment module refines global feature learning by aligning both feature embeddings and predictions across clients, thereby distilling robust, domain-invariant features. By integrating these modules, our method achieves superior generalization to unseen domains while maintaining data privacy and operating with minimal computational and communication overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_15486 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment Gupta, Sunny Sutar, Vinay Singh, Varunav Sethi, Amit Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Distributed, Parallel, and Cluster Computing I.2.6; C.1.4; D.1.3; I.5.1; H.3.4; I.2.10; I.4.0; I.4.1; I.4.2; I.4.6; I.4.7; I.4.8; I.4.9; I.4.10; I.5.1; I.5.2; I.5.4; J.2; I.2.11; I.2.10 Federated Learning (FL) offers a decentralized paradigm for collaborative model training without direct data sharing, yet it poses unique challenges for Domain Generalization (DG), including strict privacy constraints, non-i.i.d. local data, and limited domain diversity. We introduce FedAlign, a lightweight, privacy-preserving framework designed to enhance DG in federated settings by simultaneously increasing feature diversity and promoting domain invariance. First, a cross-client feature extension module broadens local domain representations through domain-invariant feature perturbation and selective cross-client feature transfer, allowing each client to safely access a richer domain space. Second, a dual-stage alignment module refines global feature learning by aligning both feature embeddings and predictions across clients, thereby distilling robust, domain-invariant features. By integrating these modules, our method achieves superior generalization to unseen domains while maintaining data privacy and operating with minimal computational and communication overhead. |
| title | FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Distributed, Parallel, and Cluster Computing I.2.6; C.1.4; D.1.3; I.5.1; H.3.4; I.2.10; I.4.0; I.4.1; I.4.2; I.4.6; I.4.7; I.4.8; I.4.9; I.4.10; I.5.1; I.5.2; I.5.4; J.2; I.2.11; I.2.10 |
| url | https://arxiv.org/abs/2501.15486 |