소장자료
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| 020 | ▼a9783031289965▼9978-3-031-28996-5▲ | ||
| 024 | 7 | ▼a10.1007/978-3-031-28996-5▼2doi▲ | |
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| 245 | 1 | 0 | ▼aTrustworthy Federated Learning▼h[electronic resource] :▼bFirst International Workshop, FL 2022, Held in Conjunction with IJCAI 2022, Vienna, Austria, July 23, 2022, Revised Selected Papers /▼cedited by Randy Goebel, Han Yu, Boi Faltings, Lixin Fan, Zehui Xiong.▲ |
| 250 | ▼a1st ed. 2023.▲ | ||
| 264 | 1 | ▼aCham :▼bSpringer International Publishing :▼bImprint: Springer,▼c2023.▲ | |
| 300 | ▼aX, 159 p. 53 illus., 49 illus. in color.▼bonline resource.▲ | ||
| 336 | ▼atext▼btxt▼2rdacontent▲ | ||
| 337 | ▼acomputer▼bc▼2rdamedia▲ | ||
| 338 | ▼aonline resource▼bcr▼2rdacarrier▲ | ||
| 347 | ▼atext file▼bPDF▼2rda▲ | ||
| 490 | 1 | ▼aLecture Notes in Artificial Intelligence,▼x2945-9141 ;▼v13448▲ | |
| 505 | 0 | ▼aAdaptive 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.▲ | |
| 520 | ▼aThis 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.▲ | ||
| 650 | 0 | ▼aArtificial intelligence.▲ | |
| 650 | 0 | ▼aData protection.▲ | |
| 650 | 0 | ▼aSocial sciences▼xData processing.▲ | |
| 650 | 0 | ▼aApplication software.▲ | |
| 650 | 1 | 4 | ▼aArtificial Intelligence.▲ |
| 650 | 2 | 4 | ▼aData and Information Security.▲ |
| 650 | 2 | 4 | ▼aComputer Application in Social and Behavioral Sciences.▲ |
| 650 | 2 | 4 | ▼aComputer and Information Systems Applications.▲ |
| 700 | 1 | ▼aGoebel, Randy.▼eeditor.▼0(orcid)0000-0002-0739-2946▼1https://orcid.org/0000-0002-0739-2946▼4edt▼4http://id.loc.gov/vocabulary/relators/edt▲ | |
| 700 | 1 | ▼aYu, Han.▼eeditor.▼0(orcid)0000-0001-6893-8650▼1https://orcid.org/0000-0001-6893-8650▼4edt▼4http://id.loc.gov/vocabulary/relators/edt▲ | |
| 700 | 1 | ▼aFaltings, Boi.▼eeditor.▼4edt▼4http://id.loc.gov/vocabulary/relators/edt▲ | |
| 700 | 1 | ▼aFan, Lixin.▼eeditor.▼4edt▼4http://id.loc.gov/vocabulary/relators/edt▲ | |
| 700 | 1 | ▼aXiong, Zehui.▼eeditor.▼4edt▼4http://id.loc.gov/vocabulary/relators/edt▲ | |
| 710 | 2 | ▼aSpringerLink (Online service)▲ | |
| 773 | 0 | ▼tSpringer Nature eBook▲ | |
| 776 | 0 | 8 | ▼iPrinted edition:▼z9783031289958▲ |
| 776 | 0 | 8 | ▼iPrinted edition:▼z9783031289972▲ |
| 830 | 0 | ▼aLecture Notes in Artificial Intelligence,▼x2945-9141 ;▼v13448▲ | |
| 856 | 4 | 0 | ▼uhttps://doi.org/10.1007/978-3-031-28996-5▲ |
Trustworthy Federated Learning[electronic resource] : First International Workshop, FL 2022, Held in Conjunction with IJCAI 2022, Vienna, Austria, July 23, 2022, Revised Selected Papers
자료유형
국외eBook
서명/책임사항
Trustworthy Federated Learning [electronic resource] : First International Workshop, FL 2022, Held in Conjunction with IJCAI 2022, Vienna, Austria, July 23, 2022, Revised Selected Papers / edited by Randy Goebel, Han Yu, Boi Faltings, Lixin Fan, Zehui Xiong.
판사항
1st ed. 2023.
형태사항
X, 159 p. 53 illus., 49 illus. in color. online resource.
총서사항
Lecture Notes in Artificial Intelligence , 2945-9141 ; 13448
Lecture Notes in Artificial Intelligence , 2945-9141 ; 13448
Lecture Notes in Artificial Intelligence , 2945-9141 ; 13448
내용주기
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.
요약주기
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.
주제
ISBN
9783031289965
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