A survey on federated learning: The journey from centralized to distributed on-site learning and beyond
المؤلف | Abdulrahman, Sawsan |
المؤلف | Tout, Hanine |
المؤلف | Ould-Slimane, Hakima |
المؤلف | Mourad, Azzam |
المؤلف | Talhi, Chamseddine |
المؤلف | Guizani, Mohsen |
تاريخ الإتاحة | 2022-11-06T11:28:46Z |
تاريخ النشر | 2021-04-01 |
اسم المنشور | IEEE Internet of Things Journal |
المعرّف | http://dx.doi.org/10.1109/JIOT.2020.3030072 |
الاقتباس | AbdulRahman, S., Tout, H., Ould-Slimane, H., Mourad, A., Talhi, C., & Guizani, M. (2020). A survey on federated learning: The journey from centralized to distributed on-site learning and beyond. IEEE Internet of Things Journal, 8(7), 5476-5497. |
الملخص | Driven by privacy concerns and the visions of deep learning, the last four years have witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An emerging model, called federated learning (FL), is rising above both centralized systems and on-site analysis, to be a new fashioned design for ML implementation. It is a privacy-preserving decentralized approach, which keeps raw data on devices and involves local ML training while eliminating data communication overhead. A federation of the learned and shared models is then performed on a central server to aggregate and share the built knowledge among participants. This article starts by examining and comparing different ML-based deployment architectures, followed by in-depth and in-breadth investigation on FL. Compared to the existing reviews in the field, we provide in this survey a new classification of FL topics and research fields based on thorough analysis of the main technical challenges and current related work. In this context, we elaborate comprehensive taxonomies covering various challenging aspects, contributions, and trends in the literature, including core system models and designs, application areas, privacy and security, and resource management. Furthermore, we discuss important challenges and open research directions toward more robust FL systems. |
اللغة | en |
الناشر | Institute of Electrical and Electronics Engineers Inc. |
الموضوع | Artificial intelligence (AI) deep learning (DL) distributed intelligence federated learning (FL) applications FL machine learning (ML) privacy resource management security |
النوع | Article |
الصفحات | 5476-5497 |
رقم العدد | 7 |
رقم المجلد | 8 |
الملفات في هذه التسجيلة
الملفات | الحجم | الصيغة | العرض |
---|---|---|---|
لا توجد ملفات لها صلة بهذه التسجيلة. |
هذه التسجيلة تظهر في المجموعات التالية
-
علوم وهندسة الحاسب [2426 items ]