Summary of Distributed Deep Reinforcement Learning Based Gradient Quantization For Federated Learning Enabled Vehicle Edge Computing, by Cui Zhang et al.
Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing
by Cui Zhang, Wenjun Zhang, Qiong Wu, Pingyi Fan, Qiang Fan, Jiangzhou Wang, Khaled B. Letaief
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Networking and Internet Architecture (cs.NI)
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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 this paper, researchers tackle the challenge of federated learning (FL) for vehicle edge computing (VEC), aiming to protect the privacy of vehicles while reducing latency. They propose a distributed deep reinforcement learning (DRL)-based quantization level allocation scheme to optimize total training time and quantization error (QE). The authors demonstrate the feasibility and effectiveness of their approach through extensive simulations, highlighting the optimal weighted factors between total training time and QE. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to make vehicle edge computing more private and efficient. Instead of sharing all data, vehicles can share tiny pieces of information that help learn from each other without revealing too much. The problem is that these tiny bits need to be sent quickly over the internet, which takes a lot of time. To solve this issue, researchers have come up with a way to shrink these bits even further, making it faster and more private. They use a special kind of AI called deep reinforcement learning to figure out the best way to do this. The results show that their approach works well and can be used in real-life applications. |
Keywords
* Artificial intelligence * Federated learning * Quantization * Reinforcement learning
