Summary of Provable Mutual Benefits From Federated Learning in Privacy-sensitive Domains, by Nikita Tsoy et al.
Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains
by Nikita Tsoy, Anna Mihalkova, Teodora Todorova, Nikola Konstantinov
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Cryptography and Security (cs.CR); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
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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 paper investigates cross-silo federated learning (FL) protocol design to balance privacy guarantees with model accuracy for mutually beneficial outcomes. It explores when and how a server can create FL protocols that are advantageous for all participants, considering mean estimation and convex stochastic optimization. The study provides necessary and sufficient conditions for the existence of such protocols and derives utility-maximizing and end-model accuracy-maximizing protocols. Synthetic experiments demonstrate the benefits of these protocols. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a way to share information between different groups while keeping it private, using something called federated learning. It’s like a team effort where everyone contributes their own data, but nobody shares too much. The goal is to make sure that everyone gets accurate results and keeps their privacy safe. The researchers found some rules that help create good outcomes for all parties involved. |
Keywords
* Artificial intelligence * Federated learning * Optimization
