Summary of Fairness and Privacy Guarantees in Federated Contextual Bandits, by Sambhav Solanki et al.
Fairness and Privacy Guarantees in Federated Contextual Bandits
by Sambhav Solanki, Shweta Jain, Sujit Gujar
First submitted to arxiv on: 5 Feb 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 This paper proposes a federated learning approach for the contextual multi-armed bandit (CMAB) problem, ensuring fairness and privacy guarantees. The desired fair outcome is merit-based exposure, which provides proportional exposure to each action based on its reward. Fairness regret is used to measure algorithm effectiveness, capturing the difference between the optimal policy and the output policy. Federated learning enables more effective solutions with Fed-FairX-LinUCB, which also ensures differential privacy. The primary challenge is designing a communication protocol for sharing information across agents without compromising privacy or fairness. A novel protocol is designed, providing sub-linear theoretical bounds on fairness regret for both Fed-FairX-LinUCB and its private counterpart, Priv-FairX-LinUCB. Simulations demonstrate the efficacy of the proposed algorithm, achieving near-optimal fairness regret. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a way to make sure that people who are using computers together (like a big team) get equal opportunities to try out different things and don’t have their privacy invaded. They want to find the best way to do this so everyone gets treated fairly. To solve this problem, they come up with a new idea called Fed-FairX-LinUCB, which is like a super-smart algorithm that makes sure everyone is treated equally while keeping private information safe. The hard part was figuring out how to share information between the computers without making things worse. They came up with a clever way to do this and tested it by running lots of simulations. It looks like their new algorithm works really well! |
Keywords
* Artificial intelligence * Federated learning
