Summary of Edge-device Collaborative Computing For Multi-view Classification, by Marco Palena et al.
Edge-device Collaborative Computing for Multi-view Classification
by Marco Palena, Tania Cerquitelli, Carla Fabiana Chiasserini
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 proposed paper tackles the challenges of pushing deep learning computations to the edge of the network for IoT devices. To achieve this, it explores collaborative inference at the edge by sharing correlated data and computation between nodes and end devices. The authors introduce selective schemes that reduce bandwidth consumption by decreasing data redundancy. For a reference scenario, they focus on multi-view classification in a networked system where sensing nodes capture overlapping fields of view. The proposed schemes are evaluated based on accuracy, computational expenditure, communication overhead, inference latency, robustness, and noise sensitivity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using deep learning for IoT devices. It’s trying to make computers at the edge of the internet smarter by sharing data and calculations between devices. This will help with things like speed, battery life, and security. The authors are testing different ways to do this and comparing how well they work. |
Keywords
» Artificial intelligence » Classification » Deep learning » Inference
