Summary of Federated Linear Contextual Bandits with Heterogeneous Clients, by Ethan Blaser et al.
Federated Linear Contextual Bandits with Heterogeneous Clients
by Ethan Blaser, Chuanhao Li, Hongning Wang
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed approach in this paper introduces a new framework for federated bandit learning that caters to heterogeneous clients. Unlike previous works that rely on strong assumptions of client homogeneity, this method clusters clients for collaborative bandit learning under the federated learning setting. The algorithm achieves sub-linear regret and communication cost for all clients, making it more practical for real-world applications. This breakthrough has significant implications for private, efficient, and decentralized online learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to help many devices or machines learn together without sharing their personal data. Usually, these devices would need to agree on the same model before they can work together, but this approach lets them learn from each other even if they have different models. This makes it more useful for real-world applications where devices may have different goals or needs. The algorithm is designed to be efficient and private, which means it doesn’t require sharing personal data. |
Keywords
* Artificial intelligence * Federated learning * Online learning
