Summary of Green Federated Learning: a New Era Of Green Aware Ai, by Dipanwita Thakur et al.
Green Federated Learning: A new era of Green Aware AI
by Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino, Francesco Piccialli
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 abstract presents the growing need to develop AI applications that prioritize environmental sustainability, particularly in large-scale wireless networks. Machine learning is a significant contributor to energy consumption, posing a challenge to the development of sustainable intelligent systems. Federated Learning (FL) offers opportunities for addressing this issue by integrating green considerations from the architectural phase onwards. The paper aims to provide a roadmap for understanding existing efforts and gaps in green-aware AI algorithms, with a focus on IoT research. It analyzes over a hundred FL works, assessing their contributions to green-aware artificial intelligence for sustainable environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper discusses how AI applications can be made more environmentally friendly. Right now, machine learning is using up a lot of energy, which is not good for the environment. Federated Learning is a new way of doing things that might help solve this problem. The authors look at over 100 different papers on FL and see how they contribute to making AI more sustainable. They also talk about some challenges that need to be addressed in order to make AI more environmentally friendly. |
Keywords
» Artificial intelligence » Federated learning » Machine learning
