Summary of On the Convergence Of a Federated Expectation-maximization Algorithm, by Zhixu Tao et al.
On the Convergence of a Federated Expectation-Maximization Algorithm
by Zhixu Tao, Rajita Chandak, Sanjeev Kulkarni
First submitted to arxiv on: 11 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: 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 studies the convergence rate of Expectation-Maximization (EM) algorithm for Federated Mixture of K Linear Regressions model (FMLR) in the presence of data heterogeneity. Specifically, it characterizes the convergence rate under various regimes of m/n, where m is the number of clients and n is the number of data points per client. The authors show that with a signal-to-noise-ratio (SNR) of order Omega(sqrt(K)), the well-initialized EM algorithm converges within the minimax distance of the ground truth under all regimes. Additionally, they identify that when the number of clients grows reasonably with respect to the number of data points per client, the EM algorithm only requires a constant number of iterations to converge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores how Federated Learning algorithms work in situations where different devices or users have different types of data. The researchers look at how well the Expectation-Maximization (EM) algorithm can handle this kind of data heterogeneity when used with the Federated Mixture of K Linear Regressions model (FMLR). They find that, under certain conditions, the EM algorithm can converge quickly and accurately, even in situations where there is a lot of variation in the data. This could have important implications for how we use machine learning to analyze and process large amounts of data from different sources. |
Keywords
» Artificial intelligence » Federated learning » Machine learning
