Summary of Augmenting the Fedprox Algorithm by Minimizing Convergence, By Anomitra Sarkar and Lavanya Vajpayee
Augmenting the FedProx Algorithm by Minimizing Convergence
by Anomitra Sarkar, Lavanya Vajpayee
First submitted to arxiv on: 2 Jun 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 novel approach called G Federated Proximity is proposed, building upon FedProx technique with slight modifications for improved efficiency and effectiveness. The algorithm leverages FTL and normalization techniques to enhance accuracy on real-time devices and heterogeneous networks. Experimental results show a significant increase in throughput, approximately 90% better convergence compared to existing models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Internet of Things has experienced rapid growth, leading to the Industrial IoT initiative. To efficiently process large amounts of data, algorithms are needed that provide better performance without sacrificing speed. A new approach called G Federated Proximity is presented, which builds upon FedProx technique and introduces modifications for improved efficiency. The algorithm uses FTL and normalization techniques to enhance accuracy on real-time devices and heterogeneous networks. |
