Summary of Fcom: a Federated Collaborative Online Monitoring Framework Via Representation Learning, by Tanapol Kosolwattana et al.
FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning
by Tanapol Kosolwattana, Huazheng Wang, Raed Al Kontar, Ying Lin
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A newly proposed federated collaborative online monitoring method enables the online learning of heterogeneous processes from decentralized data, effectively balancing exploration and exploitation in a large population of processes. By employing representation learning to capture latent representative models and a novel UCB algorithm for estimation, this approach can efficiently monitor complex systems like cognitive degradation in Alzheimer’s disease. The proposed method combines insights from federated learning and multi-armed bandit algorithms to address the limitations of existing online learning methods designed for centralized or homogeneous settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers developed a way to learn from data collected separately by many processes, without sharing all the information. This is useful when you have many things happening at once, like monitoring different people’s cognitive abilities in Alzheimer’s disease. The method uses two ideas: learning what makes each process unique, and finding the best approach for each one. |
Keywords
» Artificial intelligence » Federated learning » Online learning » Representation learning
