Task-Agnostic Federation over Decentralized Data: Research Landscape and Visions
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908733036036096 |
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| author | Wu, Wentai He, Ligang Long, Saiqin Abdelmoniem, Ahmed M. Wu, Yingliang Mao, Rui Li, Keqin |
| author_facet | Wu, Wentai He, Ligang Long, Saiqin Abdelmoniem, Ahmed M. Wu, Yingliang Mao, Rui Li, Keqin |
| contents | Increasing legislation and regulations on private and proprietary information results in scattered data sources also known as the "data islands". Although Federated Learning-based paradigms can enable privacy-preserving collaboration over decentralized data, they have inherent deficiencies in fairness, costs and reproducibility because of being learning-centric, which greatly limits the way how participants cooperate with each other. In light of this, we investigate the possibilities to shift from resource-intensive learning to task-agnostic collaboration especially when the participants have no interest in a common goal. We term this new scenario as Task-Agnostic Federation (TAF), and investigate several branches of research that serve as the technical building blocks. These techniques directly or indirectly embrace data-centric approaches that can operate independently of any learning task. In this article, we first describe the system architecture and problem setting for TAF. Then, we present a three-way roadmap and categorize recent studies in three directions: collaborative data expansion, collaborative data refinement, and collective data harmonization in the federation. Further, we highlight several challenges and open questions that deserve more attention from the community. With our investigation, we intend to offer new insights about how autonomic parties with varied motivation can cooperate over decentralized data beyond learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03140 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Task-Agnostic Federation over Decentralized Data: Research Landscape and Visions Wu, Wentai He, Ligang Long, Saiqin Abdelmoniem, Ahmed M. Wu, Yingliang Mao, Rui Li, Keqin Distributed, Parallel, and Cluster Computing Artificial Intelligence Machine Learning Increasing legislation and regulations on private and proprietary information results in scattered data sources also known as the "data islands". Although Federated Learning-based paradigms can enable privacy-preserving collaboration over decentralized data, they have inherent deficiencies in fairness, costs and reproducibility because of being learning-centric, which greatly limits the way how participants cooperate with each other. In light of this, we investigate the possibilities to shift from resource-intensive learning to task-agnostic collaboration especially when the participants have no interest in a common goal. We term this new scenario as Task-Agnostic Federation (TAF), and investigate several branches of research that serve as the technical building blocks. These techniques directly or indirectly embrace data-centric approaches that can operate independently of any learning task. In this article, we first describe the system architecture and problem setting for TAF. Then, we present a three-way roadmap and categorize recent studies in three directions: collaborative data expansion, collaborative data refinement, and collective data harmonization in the federation. Further, we highlight several challenges and open questions that deserve more attention from the community. With our investigation, we intend to offer new insights about how autonomic parties with varied motivation can cooperate over decentralized data beyond learning. |
| title | Task-Agnostic Federation over Decentralized Data: Research Landscape and Visions |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2503.03140 |