Summary of Drl-based Resource Allocation For Motion Blur Resistant Federated Self-supervised Learning in Iov, by Xueying Gu et al.
DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoV
by Xueying Gu, Qiong Wu, Pingyi Fan, Qiang Fan, Nan Cheng, Wen Chen, Khaled B. Letaief
First submitted to arxiv on: 17 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); 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 A Federated Self-Supervised Learning framework is introduced for privacy-preserving model aggregation in Internet of Vehicles (IoV) applications. This approach eliminates the need for labeled data by leveraging Self-Supervised Learning (SSL) techniques, such as Momentum Contrast (MoCo), which reduces computational demands and storage requirements. However, MoCo-based FSSL poses a risk of privacy leakage due to uploading local dictionaries from vehicles to Base Station (BS). Simplified Contrast (SimCo) addresses this issue by using dual temperatures instead of a dictionary. Additionally, a motion blur-resistant FSSL method (BFSSL) is proposed, which takes into account the negative impact of motion blur on model aggregation. To further optimize energy consumption and delay, a Deep Reinforcement Learning (DRL)-based resource allocation scheme (DRL-BFSSL) is introduced. Simulation results demonstrate the effectiveness of these proposed methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists developed a new way to train models for self-driving cars without sharing private data. This approach uses special algorithms that can learn from data without labels, making it more efficient and secure. The system also reduces energy consumption and minimizes delays by optimizing resource allocation. The results show that this method is effective in improving model performance while protecting privacy. |
Keywords
* Artificial intelligence * Reinforcement learning * Self supervised
