Federated and Transfer Learning
Roozbeh Razavi-Far · Boyu Wang · Matthew E. Taylor · Qiang Yang
Sep 2022 · Adaptation, Learning, and Optimization Buku 27 · Springer Nature
e-Buku
371
Halaman
reportRating dan ulasan tidak disahkan Ketahui Lebih Lanjut
Perihal e-buku ini
This book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of federated learning as well as its connection with transfer learning and reinforcement learning. Over the last few years, the machine learning community has become fascinated by federated and transfer learning. Transfer and federated learning have achieved great success and popularity in many different fields of application. The intended audience of this book is students and academics aiming to apply federated and transfer learning to solve different kinds of real-world problems, as well as scientists, researchers, and practitioners in AI industries, autonomous vehicles, and cyber-physical systems who wish to pursue new scientific innovations and update their knowledge on federated and transfer learning and their applications.
Berikan rating untuk e-Buku ini
Beritahu kami pendapat anda.
Maklumat pembacaan
Telefon pintar dan tablet
Pasang apl Google Play Books untuk Android dan iPad/iPhone. Apl ini menyegerak secara automatik dengan akaun anda dan membenarkan anda membaca di dalam atau luar talian, walau di mana jua anda berada.
Komputer riba dan komputer
Anda boleh mendengar buku audio yang dibeli di Google Play menggunakan penyemak imbas web komputer anda.
eReader dan peranti lain
Untuk membaca pada peranti e-dakwat seperti Kobo eReaders, anda perlu memuat turun fail dan memindahkan fail itu ke peranti anda. Sila ikut arahan Pusat Bantuan yang terperinci untuk memindahkan fail ke e-Pembaca yang disokong.
