Summary of Implementation Of Big Ai Models For Wireless Networks with Collaborative Edge Computing, by Liekang Zeng et al.
Implementation of Big AI Models for Wireless Networks with Collaborative Edge Computing
by Liekang Zeng, Shengyuan Ye, Xu Chen, Yang Yang
First submitted to arxiv on: 27 Apr 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 Big Artificial Intelligence (AI) models are crucial in intelligent applications like voice assistants and autonomous robotics. However, training these large-scale models on edge devices is challenging due to limited computing resources and intensive workload. Traditional approaches send data to remote clouds for centralized training, but this approach is unsustainable and private. We propose collaborative edge training, which leverages trusted edge devices as a resource pool for expedited big AI model training at the edge. This novel mechanism can be used for personalized fine-tuning and continual model refinement. We present a comprehensive framework for building collaborative edge training systems and analyze its merits and scheduling choices. Our empirical study shows that parallelism design has a significant impact on energy demand, making it an important consideration for sustainable edge-centric big AI model training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Big AI models are used in smart homes and factories. Training these large models on devices is hard because devices have limited power and lots of work to do. Instead of sending data to the cloud, we can use trusted devices near where the data was collected. This way, we can train models faster and more privately. We created a new way to do this called collaborative edge training. It uses many trusted devices together to train big AI models. We explained how to build these systems and showed that using multiple devices at once makes a big difference in how much energy is used. |
Keywords
» Artificial intelligence » Fine tuning
