Summary of Drl-based Federated Self-supervised Learning For Task Offloading and Resource Allocation in Isac-enabled Vehicle Edge Computing, by Xueying Gu et al.
DRL-Based Federated Self-Supervised Learning for Task Offloading and Resource Allocation in ISAC-Enabled Vehicle Edge Computing
by Xueying Gu, Qiong Wu, Pingyi Fan, Nan Cheng, Wen Chen, Khaled B. Letaief
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); 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 The paper proposes a Vehicle Edge Computing (VEC) system to enhance data exchange between vehicles and infrastructure in Intelligent Transportation Systems (ITS). To address computing demands, the VEC system offloads tasks to Road Side Unit (RSU), ensuring real-time services. The authors improved their previous FLSimCo algorithm by optimizing energy consumption and balancing local and RSU-based training. The enhanced algorithm reduces energy consumption, improves offloading efficiency, and increases the accuracy of Federated Self-Supervised Learning (SSL). The simulation results demonstrate the effectiveness of the proposed approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making vehicles smarter and more efficient by sharing data with road infrastructure. It’s like a big team effort to make transportation better! They created an algorithm that helps vehicles offload tasks to nearby roadside units, so they can focus on other things. This makes everything run faster and uses less energy. The results show that it really works! |
Keywords
» Artificial intelligence » Self supervised
