AI Article Synopsis
- * The paper highlights the benefits of FL, including enhanced data privacy, efficiency, and scalability, along with an overview of its principles, strategies, applications, and tools.
- * The authors stress that FL can address privacy issues effectively, making it especially valuable for high-risk applications where data confidentiality is crucial.
Article Abstract
Federated learning (FL) is a distributed machine learning process, which allows multiple nodes to work together to train a shared model without exchanging raw data. It offers several key advantages, such as data privacy, security, efficiency, and scalability, by keeping data local and only exchanging model updates through the communication network. This review paper provides a comprehensive overview of federated learning, including its principles, strategies, applications, and tools along with opportunities, challenges, and future research directions. The findings of this paper emphasize that federated learning strategies can significantly help overcome privacy and confidentiality concerns, particularly for high-risk applications.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11466570 | PMC |
| http://dx.doi.org/10.1016/j.heliyon.2024.e38137 | DOI Listing |
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