-
DISPONIBILITÀ IMMEDIATA
{{/disponibilitaBox}}
-
{{speseGratisLibroBox}}
{{/noEbook}}
{{^noEbook}}
-
Libro
-
Trustworthy Federated Learning
goebel randy (curatore); yu han (curatore); faltings boi (curatore); fan lixin (curatore); xiong zehui (curatore)
59,98 €
56,98 €
{{{disponibilita}}}
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