Data privacy in big data: Federated learning - BÜYÜKTANIR, BÜŞRA; DOĞAN, BUKET (Bilgi Kültür Sanat Yayınevi, 2023)
With the advancement of technology, the place of internet-based devices in our lives has increased day by day. With these devices, more data has been produced and thus the concept of big data has entered our lives. The big data produced includes various information as well as personal information. The working performance of artificial intelligence technology used in internet-based devices is directly proportional to large and various data. However, at this point, it is of great importance to ensure the privacy of the personal data used. Due to data privacy, in some organizations, data is used where it is produced, but data sharing is not done. This situation both negatively affects the development of artificial intelligence applications and limits the new productions that will emerge by processing the data produced in this field. As a solution to all these problems, federated learning technology has been developed. Federated learning is an up-to-date technology that enables model training without sacrificing data privacy. In this study, the working architectures of the big data concept and federated learning technology are explained, the current studies in the literature are reviewed and their usage areas are summarized. It is thought that this study will contribute to researchers who will work on federated learning for big data, which is up-to-date and open to development.
Keywords: Mühendislik ve Teknoloji; Engineering and Technology; Mühendislik, Bilişim ve Teknoloji (ENG); Engineering, Computing & Technology (ENG)