Summary of Self-interested Agents in Collaborative Machine Learning: An Incentivized Adaptive Data-centric Framework, by Nithia Vijayan and Bryan Kian Hsiang Low
Self-Interested Agents in Collaborative Machine Learning: An Incentivized Adaptive Data-Centric Framework
by Nithia Vijayan, Bryan Kian Hsiang Low
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 framework for adaptive data-centric collaborative machine learning enables self-interested agents to share data while optimizing their individual utility. The framework operates online, with each time step involving model training, agent-specific model updates, and feedback loops driving future data-sharing policies. Agents evaluate and partition their data, selecting a portion to share using a learned policy that optimizes the received model’s utility according to agent-specific evaluation functions. Meanwhile, the arbiter optimizes an expected loss function incorporating agent-specific weights to account for distributional differences. A bilevel optimization algorithm jointly learns model parameters and agent-specific weights. The framework introduces mean-zero noise to generate distinct agent-specific models, promoting valuable data sharing without requiring separate training. Non-asymptotic analyses ensure convergence of the agent-side policy optimization and arbiter-side optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way for people or computers to work together to learn from data. It’s like a game where each person has some information and they share it with others to get better at making predictions. The system makes sure that everyone gets the most out of their own data, while also helping others by sharing what they know. This helps everyone get smarter over time. |
Keywords
» Artificial intelligence » Loss function » Machine learning » Optimization
