Summary of Structured Reinforcement Learning For Incentivized Stochastic Covert Optimization, by Adit Jain and Vikram Krishnamurthy
Structured Reinforcement Learning for Incentivized Stochastic Covert Optimization
by Adit Jain, Vikram Krishnamurthy
First submitted to arxiv on: 13 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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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 stochastic gradient algorithm (SG) can be controlled to hide the estimate of the local stationary point from an eavesdropper, which is crucial in distributed optimization settings like federated learning and inventory management. The paper formulates this problem as a finite-horizon Markov decision process (MDP), where a learner queries a stochastic oracle and incentivizes it to obtain noisy gradient measurements. The optimal policy for the MDP has a monotone threshold structure, which is searched using a stochastic approximation algorithm and a multi-armed bandit approach. This method is demonstrated on a covert federated learning hate-speech classification task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers find ways to keep secrets in distributed optimization tasks like training AI models together with many computers. They use math to hide the target they’re trying to reach from someone who might be trying to steal it. This is important for things like classifying hate speech online. The paper shows how to do this by using algorithms and techniques that are designed to keep secrets. |
Keywords
» Artificial intelligence » Classification » Federated learning » Optimization
