Summary of Scafflsa: Taming Heterogeneity in Federated Linear Stochastic Approximation and Td Learning, by Paul Mangold et al.
SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning
by Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, Reda Alami, Alexey Naumov, Eric Moulines
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Optimization and Control (math.OC)
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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 analyzes the sample and communication complexities of the federated linear stochastic approximation (FedLSA) algorithm, examining how agent heterogeneity affects local training. The authors show that FedLSA’s communication complexity scales polynomially with the inverse of desired accuracy ε, but propose a new variant called SCAFFLSA that uses control variates to correct for client drift and achieves logarithmic scaling with desired accuracy. Notably, SCAFFLSA exhibits linear speed-up in sample complexity, requiring novel theoretical arguments. The authors apply this method to federated temporal difference learning with linear function approximation, highlighting the corresponding complexity improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at a special kind of machine learning called Federated Linear Stochastic Approximation (FedLSA). They want to know how hard it is to make computers communicate and share information when they’re all trying to learn from different things. The authors found that FedLSA gets harder as you try to get more accurate results, but they came up with a new way to do it called SCAFFLSA that makes it better. They also showed that this new method can make computers learn faster and more efficiently when they’re all working together. |
Keywords
* Artificial intelligence * Machine learning
