Summary of Federated Learning in Wireless Networks Via Over-the-air Computations, by Halil Yigit Oksuz et al.
Federated Learning in Wireless Networks via Over-the-Air Computations
by Halil Yigit Oksuz, Fabio Molinari, Henning Sprekeler, Jörg Raisch
First submitted to arxiv on: 8 May 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Information Theory (cs.IT); Multiagent Systems (cs.MA)
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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 In a multi-agent system, agents can work together to learn a model from data without sharing their individual datasets. Federated learning is used to improve resource-efficiency and ensure data privacy. To further increase efficiency, researchers have developed Over-the-Air Computation, which exploits the interference property of wireless channels. Unlike standard communication schemes, this approach does not require transmitting signals at distinct time or frequency slots, resulting in improved resource utilization. The proposed method does not require reconstructing channel coefficients using complex encoding-decoding schemes, leading to enhanced efficiency and privacy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a group of agents working together without sharing their individual data. This is called federated learning, which helps save resources and keeps data private. To make it even more efficient, researchers have developed a new way of communicating, called Over-the-Air Computation. It takes advantage of the way wireless signals interfere with each other. By doing things differently, this approach saves resources and ensures that data remains private. |
Keywords
* Artificial intelligence * Federated learning
