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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.12254 |
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| _version_ | 1866917011762708480 |
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| author | He, Ningxin Liu, Yang Sun, Wei Ye, Xiaozhou Ouyang, Ye Gao, Tiegang Zhang, Zehui |
| author_facet | He, Ningxin Liu, Yang Sun, Wei Ye, Xiaozhou Ouyang, Ye Gao, Tiegang Zhang, Zehui |
| contents | Text-to-Image (T2I) models have demonstrated their versatility in a wide range of applications. However, adaptation of T2I models to specialized tasks is often limited by the availability of task-specific data due to privacy concerns. On the other hand, harnessing the power of rich multimodal data from modern mobile systems and IoT infrastructures presents a great opportunity. This paper introduces Federated Multi-modal Knowledge Transfer (FedMMKT), a novel framework that enables co-enhancement of a server T2I model and client task-specific models using decentralized multimodal data without compromising data privacy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12254 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FedMMKT:Co-Enhancing a Server Text-to-Image Model and Client Task Models in Multi-Modal Federated Learning He, Ningxin Liu, Yang Sun, Wei Ye, Xiaozhou Ouyang, Ye Gao, Tiegang Zhang, Zehui Machine Learning Text-to-Image (T2I) models have demonstrated their versatility in a wide range of applications. However, adaptation of T2I models to specialized tasks is often limited by the availability of task-specific data due to privacy concerns. On the other hand, harnessing the power of rich multimodal data from modern mobile systems and IoT infrastructures presents a great opportunity. This paper introduces Federated Multi-modal Knowledge Transfer (FedMMKT), a novel framework that enables co-enhancement of a server T2I model and client task-specific models using decentralized multimodal data without compromising data privacy. |
| title | FedMMKT:Co-Enhancing a Server Text-to-Image Model and Client Task Models in Multi-Modal Federated Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.12254 |