Summary of Tackling System and Statistical Heterogeneity For Federated Learning with Adaptive Client Sampling, by Bing Luo et al.
Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling
by Bing Luo, Wenli Xiao, Shiqiang Wang, Jianwei Huang, Leandros Tassiulas
First submitted to arxiv on: 21 Dec 2021
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Networking and Internet Architecture (cs.NI); Optimization and Control (math.OC)
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 This research paper proposes an adaptive client sampling algorithm for federated learning (FL) that tackles both system and statistical heterogeneity to minimize the wall-clock convergence time. The authors analyze the convergence of FL algorithms with arbitrary client sampling probabilities, obtaining a new tractable convergence bound. They establish a relationship between the total learning time and sampling probabilities, which leads to a non-convex optimization problem for training time minimization. The proposed algorithm efficiently learns unknown parameters in the convergence bound and approximately solves the non-convex problem. Experimental results from both hardware prototype and simulation demonstrate that the proposed scheme significantly reduces the convergence time compared to several baseline sampling schemes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers learn together without sharing their private data by designing a new way for choosing which devices participate in each learning step. When there are many devices, they usually only take part some of the time. This slows down how long it takes for all the devices to agree on what they’ve learned. The authors want to speed this up by creating an algorithm that chooses which devices participate in a way that’s best for everyone. They also come up with new math to understand how fast or slow this process will be, depending on which devices are chosen. They test their idea and show it works better than other ways of choosing devices. |
Keywords
* Artificial intelligence * Federated learning * Optimization
