Summary of A New Theoretical Perspective on Data Heterogeneity in Federated Optimization, by Jiayi Wang et al.
A New Theoretical Perspective on Data Heterogeneity in Federated Optimization
by Jiayi Wang, Shiqiang Wang, Rong-Rong Chen, Mingyue Ji
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Information Theory (cs.IT); 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 proposed research aims to bridge the gap between theoretical understanding and practical performance in federated learning (FL) by analyzing data heterogeneity from a new perspective. The authors introduce a weaker assumption, named the heterogeneity-driven pseudo-Lipschitz assumption, which jointly characterizes the effect of data heterogeneity with gradient divergence assumptions. This assumption replaces the local Lipschitz constant with a much smaller heterogeneity-driven pseudo-Lipschitz constant, leading to a significantly reduced convergence upper bound for FedAvg and its extensions. The research also provides insights on the impact of data heterogeneity when the local objective function is quadratic, identifying a region where FedAvg can outperform mini-batch SGD even when gradient divergence is arbitrarily large. Experimental validation supports the findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning (FL) helps computers learn together without sharing all their data. But when different devices have different kinds of data, it’s hard to make sure everyone learns correctly. Researchers have studied this problem before and found that it gets harder as more information is shared between devices. However, in real-life tests, they saw that using more local information actually helped the computers learn better. This paper tries to understand why this happens by introducing a new way of thinking about how data is different. It shows that this approach can help us understand how FL algorithms work and how we can make them better. |
Keywords
» Artificial intelligence » Federated learning » Objective function
