Summary of Not All Federated Learning Algorithms Are Created Equal: a Performance Evaluation Study, by Gustav A. Baumgart et al.
Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Study
by Gustav A. Baumgart, Jaemin Shin, Ali Payani, Myungjin Lee, Ramana Rao Kompella
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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 paper presents a comprehensive evaluation of several canonical Federated Learning (FL) algorithms, including FedAvg, FedProx, FedYogi, FedAdam, SCAFFOLD, and FedDyn. The authors leverage an open-source framework called Flame to assess the performance of these algorithms across various metrics. Notably, no single algorithm emerges as the best performer across all metrics, highlighting the complexity of FL optimization. The study reveals that state-of-the-art algorithms often trade off accuracy for computation or communication overheads. Additionally, recent algorithms exhibit smaller standard deviations in accuracy, indicating greater stability. However, these advanced algorithms are more prone to catastrophic failures without additional techniques like gradient clipping. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well different types of machine learning work when trying to learn from lots of small datasets instead of one big one. They tested six different ways of doing this and found that no single way is best for everything. Some ways are better if you want high accuracy, but they use a lot more computer power or send data back and forth between devices. Other ways are better at staying consistent across different devices, but can still fail completely if something goes wrong. |
Keywords
» Artificial intelligence » Federated learning » Machine learning » Optimization
