Federated Learning: Privacy and Incentive
Qiang Yang, Lixin Fan, Han Yu

Informasi Umum

Kode

21.21.3900

Klasifikasi

005.8 - Data Security

Jenis

Buku - Elektronik (E-Book)

Subjek

Data Security

No. Rak

Dilihat

179 kali

Informasi Lainnya

Abstraksi

This book provides a comprehensive and self-contained introduction to Federated Learning, ranging from the basic knowledge and theories to various key applications, and the privacy and incentive factors are the focus of the whole book. This book is timely needed since Federated Learning is getting popular after the release of the General Data Protection Regulation (GDPR). As Federated Learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. First, it introduces different privacy-preserving methods for protecting a Federated Learning model against different types of attacks such as Data Leakage and/or Data Poisoning. Second, the book presents incentive mechanisms which aim to encourage individuals to participate in the Federated Learning ecosystems. Last but not the least, this book also describes how Federated Learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both academia and industries, who would like to learn federated learning from scratch, practice its implementation, and apply it in their own business.

Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing are preferred.

• CMI6E3 - SEKURITAS DAN PRIVASI UNTUK PENGGUNA TEKNOLOGI INFORMASI

Koleksi & Sirkulasi

Tersedia 1 dari total 1 Koleksi

Pengarang

Nama	Qiang Yang, Lixin Fan, Han Yu
Jenis	Perorangan
Penyunting
Penerjemah

Penerbit

Nama	Springer
Kota	New York
Tahun	2020

Sirkulasi

Harga sewa	IDR 0,00
Denda harian	IDR 0,00
Jenis	Non-Sirkulasi

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