Summary of Quantized and Asynchronous Federated Learning, by Tomas Ortega and Hamid Jafarkhani
Quantized and Asynchronous Federated Learning
by Tomas Ortega, Hamid Jafarkhani
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP); 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 Recent advances in federated learning have led to the development of asynchronous variants, which offer improved scalability and speed compared to their synchronous counterparts. However, these designs do not account for quantization, a crucial aspect necessary to mitigate communication bottlenecks. To bridge this gap, we introduce Quantized Asynchronous Federated Learning (QAFeL), a novel algorithm that incorporates a hidden-state quantization scheme to prevent error propagation caused by direct quantization. QAFeL also employs a buffer to aggregate client updates, ensuring scalability and compatibility with techniques such as secure aggregation. Our theoretical analysis demonstrates that QAFeL achieves an (1/) ergodic convergence rate for stochastic gradient descent on non-convex objectives, which is the optimal order of complexity, without requiring bounded gradients or uniform client arrivals. We validate our findings on standard benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning has made big progress recently! Researchers have found ways to make it work faster and better when many devices learn together at the same time. But there’s a problem – they haven’t figured out how to deal with the limitations of communication between these devices. To solve this, we developed a new way called QAFeL (Quantized Asynchronous Federated Learning). This method helps prevent mistakes from happening because of the way information is transmitted and received. We also showed that our approach works well on standard tests. |
Keywords
» Artificial intelligence » Federated learning » Quantization » Stochastic gradient descent
