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Trustworthy Federated Learning: First International Workshop, FL 2022, Held in Conjunction with IJCAI 2022, Vienna, Austria, July 23, 2022, Revised Selected Papers 1st ed. 2023 [Minkštas viršelis]

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  • Formatas: Paperback / softback, 159 pages, aukštis x plotis: 235x155 mm, weight: 273 g, 49 Illustrations, color; 4 Illustrations, black and white; X, 159 p. 53 illus., 49 illus. in color., 1 Paperback / softback
  • Serija: Lecture Notes in Artificial Intelligence 13448
  • Išleidimo metai: 29-Mar-2023
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3031289951
  • ISBN-13: 9783031289958
Kitos knygos pagal šią temą:
Trustworthy Federated Learning: First International Workshop, FL 2022, Held in Conjunction with IJCAI 2022, Vienna, Austria, July 23, 2022, Revised Selected Papers 1st ed. 2023Didesnis paveikslėlis
  • Formatas: Paperback / softback, 159 pages, aukštis x plotis: 235x155 mm, weight: 273 g, 49 Illustrations, color; 4 Illustrations, black and white; X, 159 p. 53 illus., 49 illus. in color., 1 Paperback / softback
  • Serija: Lecture Notes in Artificial Intelligence 13448
  • Išleidimo metai: 29-Mar-2023
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3031289951
  • ISBN-13: 9783031289958
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9783031289958.html
Raktažodžiai:

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.


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.