dorsal/arxiv
View SchemaMulti-Modal Style Transfer-based Prompt Tuning for Efficient Federated Domain Generalization
| Authors | Yuliang Chen, Xi Lin, Jun Wu, Xiangrui Cai, Qiaolun Zhang, Xichun Fan, Jiapeng Xu, Xiu Su |
|---|---|
| Categories | |
| ArXiv ID | 2601.05955vv1 |
| URL | https://arxiv.org/abs/2601.05955 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
Federated Domain Generalization (FDG) aims to collaboratively train a global model across distributed clients that can generalize well on unseen domains. However, existing FDG methods typically struggle with cross-client data heterogeneity and incur significant communication and computation overhead. To address these challenges, this paper presents a new FDG framework, dubbed FaST-PT, which facilitates local feature augmentation and efficient unseen domain adaptation in a distributed manner. First, we propose a lightweight Multi-Modal Style Transfer (MST) method to transform image embedding under text supervision, which could expand the training data distribution and mitigate domain shift. We then design a dual-prompt module that decomposes the prompt into global and domain prompts. Specifically, global prompts capture general knowledge from augmented embedding across clients, while domain prompts capture domain-specific knowledge from local data. Besides, Domain-aware Prompt Generation (DPG) is introduced to adaptively generate suitable prompts for each sample, which facilitates unseen domain adaptation through knowledge fusion. Extensive experiments on four cross-domain benchmark datasets, e.g., PACS and DomainNet, demonstrate the superior performance of FaST-PT over SOTA FDG methods such as FedDG-GA and DiPrompt. Ablation studies further validate the effectiveness and efficiency of FaST-PT.
{
"annotation_id": "033c8bfb-7bb1-41c3-a212-0cce640526f7",
"date_created": "2026-02-17T05:53:04.954000Z",
"date_modified": "2026-02-17T05:53:04.954000Z",
"file_hash": "01b4ea15f62ffcabe1d59bbe27791dd391d16029d710818e52b8231bc0a51ef5",
"private": false,
"record": {
"abstract": "Federated Domain Generalization (FDG) aims to collaboratively train a global model across distributed clients that can generalize well on unseen domains. However, existing FDG methods typically struggle with cross-client data heterogeneity and incur significant communication and computation overhead. To address these challenges, this paper presents a new FDG framework, dubbed FaST-PT, which facilitates local feature augmentation and efficient unseen domain adaptation in a distributed manner. First, we propose a lightweight Multi-Modal Style Transfer (MST) method to transform image embedding under text supervision, which could expand the training data distribution and mitigate domain shift. We then design a dual-prompt module that decomposes the prompt into global and domain prompts. Specifically, global prompts capture general knowledge from augmented embedding across clients, while domain prompts capture domain-specific knowledge from local data. Besides, Domain-aware Prompt Generation (DPG) is introduced to adaptively generate suitable prompts for each sample, which facilitates unseen domain adaptation through knowledge fusion. Extensive experiments on four cross-domain benchmark datasets, e.g., PACS and DomainNet, demonstrate the superior performance of FaST-PT over SOTA FDG methods such as FedDG-GA and DiPrompt. Ablation studies further validate the effectiveness and efficiency of FaST-PT.",
"arxiv_id": "2601.05955",
"authors": [
"Yuliang Chen",
"Xi Lin",
"Jun Wu",
"Xiangrui Cai",
"Qiaolun Zhang",
"Xichun Fan",
"Jiapeng Xu",
"Xiu Su"
],
"categories": [
"cs.DC"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Multi-Modal Style Transfer-based Prompt Tuning for Efficient Federated Domain Generalization",
"url": "https://arxiv.org/abs/2601.05955",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "fbd02bf7-d895-4577-b7b3-62654f33b7e6",
"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
},
"user_id": 1000002
}