FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916266915135488 |
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| author | Kosolwattana, Tanapol Wang, Huazheng Kontar, Raed Al Lin, Ying |
| author_facet | Kosolwattana, Tanapol Wang, Huazheng Kontar, Raed Al Lin, Ying |
| contents | Online learning has demonstrated notable potential to dynamically allocate limited resources to monitor a large population of processes, effectively balancing the exploitation of processes yielding high rewards, and the exploration of uncertain processes. However, most online learning algorithms were designed under 1) a centralized setting that requires data sharing across processes to obtain an accurate prediction or 2) a homogeneity assumption that estimates a single global model from the decentralized data. To facilitate the online learning of heterogeneous processes from the decentralized data, we propose a federated collaborative online monitoring method, which captures the latent representative models inherent in the population through representation learning and designs a novel federated collaborative UCB algorithm to estimate the representative models from sequentially observed decentralized data. The efficiency of our method is illustrated through theoretical analysis, simulation studies, and decentralized cognitive degradation monitoring in Alzheimer's disease. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_20504 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning Kosolwattana, Tanapol Wang, Huazheng Kontar, Raed Al Lin, Ying Machine Learning Online learning has demonstrated notable potential to dynamically allocate limited resources to monitor a large population of processes, effectively balancing the exploitation of processes yielding high rewards, and the exploration of uncertain processes. However, most online learning algorithms were designed under 1) a centralized setting that requires data sharing across processes to obtain an accurate prediction or 2) a homogeneity assumption that estimates a single global model from the decentralized data. To facilitate the online learning of heterogeneous processes from the decentralized data, we propose a federated collaborative online monitoring method, which captures the latent representative models inherent in the population through representation learning and designs a novel federated collaborative UCB algorithm to estimate the representative models from sequentially observed decentralized data. The efficiency of our method is illustrated through theoretical analysis, simulation studies, and decentralized cognitive degradation monitoring in Alzheimer's disease. |
| title | FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.20504 |