Federated Learning
Federated Learning (FL) represents a transformative leap in distributed machine learning by enabling multiple clients to collaboratively solve complex tasks without compromising data privacy. This innovative approach eliminates the need for centralized cloud storage, ensuring privacy-preserving data...
محفوظ في:
| التنسيق: | Online |
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| اللغة: | الإنجليزية |
| منشور في: |
IntechOpen
2025
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| الموضوعات: | |
| الوصول للمادة أونلاين: | ONIX_20250617T171318_9781836342113_49 |
| الوسوم: |
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| _version_ | 1869519530120708096 |
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| collection | Directory of Open Access Books |
| description | Federated Learning (FL) represents a transformative leap in distributed machine learning by enabling multiple clients to collaboratively solve complex tasks without compromising data privacy. This innovative approach eliminates the need for centralized cloud storage, ensuring privacy-preserving data handling while offering smarter models, reduced latency, and enhanced power efficiency. This book serves as a comprehensive guide to the evolving field of Federated Learning, providing in-depth insights into its definition, architecture, and classification. It examines the distinctions between FL and traditional distributed learning paradigms through a comparative lens. The chapters explore key concepts, algorithmic advancements, and computational strategies that underpin the development of FL, with a particular focus on deep learning applications. Readers will find detailed discussions on critical topics such as horizontal and vertical FL, federated neural networks, federated reinforcement learning, and specialized algorithms like Federated LSTM and CNNs. By bridging theoretical foundations with practical implementations, the book also addresses common challenges in FL and presents potential pathways for future advancements. Aimed at researchers, academics, and practitioners, this book is valuable for understanding Federated Learning's role in shaping the future of privacy-conscious, intelligent machine learning systems. |
| format | Online |
| id | doab-20.500.12854ir-161477 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | IntechOpen |
| publisherStr | IntechOpen |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1614772025-06-17T15:30:11Z Federated Learning Ahmad, Sultan Alharbi, Meshal Jha, Sudan Ali, Aleem Damaševičius, Robertas Artificial intelligence thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence Federated Learning (FL) represents a transformative leap in distributed machine learning by enabling multiple clients to collaboratively solve complex tasks without compromising data privacy. This innovative approach eliminates the need for centralized cloud storage, ensuring privacy-preserving data handling while offering smarter models, reduced latency, and enhanced power efficiency. This book serves as a comprehensive guide to the evolving field of Federated Learning, providing in-depth insights into its definition, architecture, and classification. It examines the distinctions between FL and traditional distributed learning paradigms through a comparative lens. The chapters explore key concepts, algorithmic advancements, and computational strategies that underpin the development of FL, with a particular focus on deep learning applications. Readers will find detailed discussions on critical topics such as horizontal and vertical FL, federated neural networks, federated reinforcement learning, and specialized algorithms like Federated LSTM and CNNs. By bridging theoretical foundations with practical implementations, the book also addresses common challenges in FL and presents potential pathways for future advancements. Aimed at researchers, academics, and practitioners, this book is valuable for understanding Federated Learning's role in shaping the future of privacy-conscious, intelligent machine learning systems. 2025-06-17T15:30:08Z 2025-06-17T15:30:08Z 2025 book ONIX_20250617T171318_9781836342113_49 2633-1403 9781836342113 9781836342120 9781836342137 https://directory.doabooks.org/handle/20.500.12854/161477 eng Artificial Intelligence image/jpeg n/a https://www.intechopen.com/books/1003817 https://intech-files.s3.amazonaws.com/a043Y000011YMzVQAW/0015721_Authors_Book%20%282025-06-11%2009%3A10%3A17%29.pdf IntechOpen IntechOpen 10.5772/intechopen.1003284 10.5772/intechopen.1003284 78a36484-2c0c-47cb-ad67-2b9f5cd4a8f6 9781836342113 9781836342120 9781836342137 IntechOpen 33 192 open access |
| spellingShingle | Artificial intelligence thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence Federated Learning |
| title | Federated Learning |
| title_full | Federated Learning |
| title_fullStr | Federated Learning |
| title_full_unstemmed | Federated Learning |
| title_short | Federated Learning |
| title_sort | federated learning |
| topic | Artificial intelligence thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence |
| topic_facet | Artificial intelligence thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence |
| url | ONIX_20250617T171318_9781836342113_49 |