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Title:
Trustworthy Federated Learning First International Workshop, FL 2022, Held in Conjunction with IJCAI 2022, Vienna, Austria, July 23, 2022, Revised Selected Papers
Author:
Goebel, Randy. editor.
ISBN:
9783031289965
Edition:
1st ed. 2023.
Physical Description:
X, 159 p. 53 illus., 49 illus. in color. online resource.
Series:
Lecture Notes in Artificial Intelligence, 13448
Contents:
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.
Added Corporate Author:
Electronic Access:
https://doi.org/10.1007/978-3-031-28996-5Copies:
Available:*
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