Summary of Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory, by Jiajun Liang et al.
Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory
by Jiajun Liang, Qian Zhang, Wei Deng, Qifan Song, Guang Lin
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation (stat.CO); Machine Learning (stat.ML)
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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 novel Bayesian federated learning algorithm, FA-HMC, is introduced, offering efficient parameter estimation and uncertainty quantification for non-iid distributed datasets. The paper provides rigorous convergence guarantees under strong convexity and Hessian smoothness assumptions. Analysis explores the impact of dimensionality, gradient noise, momentum, and communication frequency on FA-HMC’s performance and costs. Comparative studies show that FA-HMC outperforms existing FA-LD algorithm. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research introduces a new way to learn from data while keeping it private. It’s called FA-HMC. The algorithm helps with estimating model parameters and figuring out how certain those estimates are. The paper shows that this method works well even when the data is different between groups. The study also looks at how different factors affect the performance of FA-HMC, like how much information is shared and how big the model is. |
Keywords
* Artificial intelligence * Federated learning
