Summary of Fedgreen: Carbon-aware Federated Learning with Model Size Adaptation, by Ali Abbasi et al.
FedGreen: Carbon-aware Federated Learning with Model Size Adaptation
by Ali Abbasi, Fan Dong, Xin Wang, Henry Leung, Jiayu Zhou, Steve Drew
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 learning approach is developed to minimize the carbon emissions associated with training models on distributed clients, which are hosted by cloud and edge servers. The proposed method, FedGreen, uses adaptive model sizes shared with clients based on their carbon profiles and locations, achieved through ordered dropout as a model compression technique. Theoretical analysis shows that FedGreen can balance carbon emissions with convergence accuracy by optimizing parameters considering the carbon intensity discrepancy across countries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new approach is being developed to make machine learning more environmentally friendly. When training models on many devices, these devices are connected to cloud or edge servers, which consume energy and produce carbon emissions. The goal is to reduce this impact by adjusting how models are trained based on the location and power source of each device. This can be done using a technique called ordered dropout. The new approach, FedGreen, shows promise in reducing carbon emissions while maintaining model accuracy. |
Keywords
» Artificial intelligence » Dropout » Federated learning » Machine learning » Model compression
