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| Main Authors: | , |
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| Format: | Recurso digital |
| Language: | |
| Published: |
Zenodo
2023
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| Subjects: | |
| Online Access: | https://doi.org/10.5281/zenodo.10378239 |
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Table of Contents:
- <div> <div> <div> <div> <div> <div> <p>Federated learning (FL) is a potential Machine Learning (ML) approach that promotes cooperative learning among many distributed systems while ensuring data privacy. In this study, we present a wide review of the design and evaluation of FL, with a particular focus on data partitioning. We discuss the challenges and solutions associated with FL implementation and demonstrate the design and execution of our proposed FL architecture. The main contribution of this paper is an investigation of data partitioning in FL and its impact on system performance. Using real-world public opinion data, we evaluate our proposed FL architecture and investigate performance measures such as binary accuracy, F1 score, loss, communication overhead, and data transmission between the server and clients. The experimental results provide useful information on the effective use of FL in various contexts. We underline the distinct advantages of various data partitioning algorithms based on data distribution and privacy requirements. Our findings contribute to the creation of successful FL systems that protect privacy.</p> </div> </div> </div> </div> </div> </div>