• Afhalen na 30 minuten in een winkel met voorraad
  • Gratis thuislevering in België vanaf € 25
  • Ruim aanbod met meer dan 10 miljoen producten
  • Afhalen na 30 minuten in een winkel met voorraad
  • Gratis thuislevering in België vanaf € 25
  • Ruim aanbod met meer dan 10 miljoen producten
€ 106,95
+ 213 punten
Uitvoering
Eenvoudig bestellen
Veilig betalen
Gratis thuislevering vanaf € 25
Gratis levering in je Standaard Boekhandel

Omschrijving

This book shows how federated machine learning allows multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private. Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example.

In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.

Specificaties

Toon meer

Beoordelingen

Uitgebreide specificaties

Betrokkenen

Inhoud

Eigenschappen

  • Productcode (EAN): 9781681736976
  • Verschijningsdatum: 19/12/2019
  • Uitvoering: Paperback
  • Bestandsformaat: Trade paperback (VS)
  • Afmetingen: 191 mm x 235 mm
  • Gewicht: 367 g