• Genere: Libro
  • Lingua: Inglese
  • Editore: Springer
  • Pubblicazione: 03/2023
  • Edizione: 1st ed. 2023

Trustworthy Federated Learning

; ; ; ;

59,98 €
56,98 €
AGGIUNGI AL CARRELLO
TRAMA
This book constitutes the refereed proceedings of the First International Workshop, FL 2022, Held in Conjunction with IJCAI 2022, held in Vienna, Austria, during July 23-25, 2022. The 11 full papers presented in this book were carefully reviewed and selected from 12 submissions. They are organized in three topical sections: answer set programming; adaptive expert models for personalization in federated learning and privacy-preserving federated cross-domain social recommendation.

SOMMARIO
Adaptive Expert Models for Personalization in Federated Learning.- Federated Learning with GAN-based Data Synthesis for Non-iid Clients.- Practical and Secure Federated Recommendation with Personalized Mask.- A General Theory for Client Sampling in Federated Learning.- Decentralized adaptive clustering of deep nets is beneficial for client collaboration.- Sketch to Skip and Select: Communication Efficient Federated Learning using Locality Sensitive Hashing.- Fast Server Learning Rate Tuning for Coded Federated Dropout.- FedAUXfdp: Differentially Private One-Shot Federated Distillation.- Secure forward aggregation for vertical federated neural network.- Two-phased Federated Learning with Clustering and Personalization for Natural Gas Load Forecasting.- Privacy-Preserving Federated Cross-Domain Social Recommendation.

ALTRE INFORMAZIONI
  • Condizione: Nuovo
  • ISBN: 9783031289958
  • Collana: Lecture Notes in Computer Science
  • Dimensioni: 235 x 155 mm
  • Formato: Brossura
  • Illustration Notes: X, 159 p. 53 illus., 49 illus. in color.
  • Pagine Arabe: 159
  • Pagine Romane: x