Summary of Rendering Wireless Environments Useful For Gradient Estimators: a Zero-order Stochastic Federated Learning Method, by Elissa Mhanna and Mohamad Assaad
Rendering Wireless Environments Useful for Gradient Estimators: A Zero-Order Stochastic Federated Learning Method
by Elissa Mhanna, Mohamad Assaad
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Multiagent Systems (cs.MA); 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 This paper proposes a novel doubly communication-efficient zero-order (ZO) method for cross-device federated learning, which enables edge devices to collaborate and train models without sharing raw data. The ZOFL framework replaces communicating long vectors with scalar values, leveraging the wireless communication channel’s characteristics. This approach eliminates the need to analyze and mitigate channel state coefficients. The authors provide a thorough analysis of the proposed method, showing that it converges almost surely in nonconvex settings and achieves a convergence rate of O(1/√[3]K). Experimental results demonstrate the algorithm’s potential. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists have found a way for many devices to work together and learn from each other without sharing their private data. This is useful because it helps keep personal information safe while still allowing devices to get better at tasks like image recognition or speech understanding. The new method uses the natural characteristics of wireless communication channels to make the learning process more efficient, which can help overcome limitations caused by limited uplink capacity. |
Keywords
* Artificial intelligence * Federated learning
