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001461920 001__ 1461920
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001461920 019__ $$a1374243247
001461920 020__ $$a9783031289965$$q(electronic bk.)
001461920 020__ $$a303128996X$$q(electronic bk.)
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001461920 020__ $$z9783031289958
001461920 0247_ $$a10.1007/978-3-031-28996-5$$2doi
001461920 035__ $$aSP(OCoLC)1374425264
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001461920 08204 $$a006.3/1$$223/eng/20230406
001461920 1112_ $$aInternational Workshop on Trustworthy Federated Learning$$n(1st :$$d2022 :$$cVienna, Austria)
001461920 24510 $$aTrustworthy federated learning :$$bfirst International Workshop, FL 2022, held in conjunction with IJCAI 2022, Vienna, Austria, July 23, 2022, Revised selected papers /$$cRandy Goebel, Han Yu, Boi Faltings, Lixin Fan, Zehui Xiong, editors.
001461920 260__ $$aCham :$$bSpringer,$$c2023.
001461920 300__ $$a1 online resource (168 p.).
001461920 4901_ $$aLecture notes in artificial intelligence
001461920 4901_ $$aLecture Notes in Computer Science ;$$v13448
001461920 500__ $$a4.2 Privacy
001461920 5050_ $$aIntro -- Preface -- Organization -- Contents -- Adaptive Expert Models for Federated Learning -- 1 Introduction -- 2 Background -- 2.1 Problem Formulation -- 2.2 Regimes of Non-IID Data -- 2.3 Federated Learning -- 2.4 Iterative Federated Clustering -- 2.5 Federated Learning Using a Mixture of Experts -- 3 Adaptive Expert Models for Personalization -- 3.1 Framework Overview and Motivation -- 4 Experiments -- 4.1 Datasets -- 4.2 Non-IID Sampling -- 4.3 Model Architecture -- 4.4 Hyperparameter Tuning -- 4.5 Results -- 5 Related Work -- 6 Discussion -- 7 Conclusion -- References
001461920 5058_ $$aFederated Learning with GAN-Based Data Synthesis for Non-IID Clients -- 1 Instruction -- 2 Related Works -- 3 Preliminary -- 4 Synthetic Data Aided Federated Learning (SDA-FL) -- 5 Experiments -- 5.1 Experiment Setup -- 5.2 Evaluation Results -- 6 Conclusions and Discussions -- References -- Practical and Secure Federated Recommendation with Personalized Mask -- 1 Introduction -- 2 Preliminaries -- 2.1 Matrix Factorization -- 2.2 Federated Matrix Factorization -- 3 Federated Masked Matrix Factorization -- 3.1 Personalized Mask -- 3.2 Adaptive Secure Aggregation -- 4 Experiments -- 4.1 Settings
001461920 5058_ $$a4.2 Efficiency Promotion and Privacy Discussion -- 4.3 Discussion on Model Effectiveness -- 5 Conclusion -- References -- A General Theory for Client Sampling in Federated Learning -- 1 Introduction -- 2 Background -- 2.1 Aggregating Clients Local Updates -- 2.2 Unbiased Data Agnostic Client Samplings -- 2.3 Advanced Client Sampling Techniques -- 3 Convergence Guarantees -- 3.1 Asymptotic FL Convergence with Respect to Client Sampling -- 3.2 Application to Current Client Sampling Schemes -- 4 Experiments on Real Data -- 5 Conclusion -- References
001461920 5058_ $$aDecentralized Adaptive Clustering of Deep Nets is Beneficial for Client Collaboration -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Non-IID Data -- 3.2 DAC: Decentralized Adaptive Clustering -- 3.3 Variable DAC -- 4 Experimental Setup -- 5 Results on Covariate Shift -- 6 Results on Label Shift -- 7 Conclusions -- References -- Sketch to Skip and Select: Communication Efficient Federated Learning Using Locality Sensitive Hashing -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Sketch-Based Communication Skipping: Sketch-to-Skip -- 3.2 Sketch-Based Client Selection: Sketch-to-Select
001461920 5058_ $$a3.3 Sketch to Skip and Select FL Algorithm -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Results -- 5 Conclusions -- References -- Fast Server Learning Rate Tuning for Coded Federated Dropout -- 1 Introduction -- 2 Background -- 3 Methodology -- 3.1 Fast Server Learning Rate Adaptation -- 3.2 Coded Federated Dropout -- 4 Evaluation -- 5 Conclusion and Future Works -- References -- FedAUXfdp: Differentially Private One-Shot Federated Distillation -- 1 Introduction -- 2 Related Work -- 3 FedAUX -- 3.1 Method -- 3.2 Privacy -- 4 FedAUXfdp -- 4.1 Regularized Empirical Risk Minimization
001461920 506__ $$aAccess limited to authorized users.
001461920 520__ $$aThis book constitutes the refereed proceedings of the First International Workshop, FL 2022, Held in Conjunction with IJCAI 2022, held in Vienna, Austria, during July 23-25, 2022. The 11 full papers presented in this book were carefully reviewed and selected from 12 submissions. They are organized in three topical sections: answer set programming; adaptive expert models for personalization in federated learning and privacy-preserving federated cross-domain social recommendation.
001461920 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed April 6, 2023).
001461920 650_0 $$aMachine learning$$vCongresses.
001461920 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001461920 655_0 $$aElectronic books.
001461920 7001_ $$aGoebel, Randy.
001461920 7001_ $$aYu, Han$$c(Assistant Professor)
001461920 7001_ $$aFaltings, Boi.
001461920 7001_ $$aFan, Lixin$$c(Scientist)
001461920 7001_ $$aXiong, Zehui.
001461920 7112_ $$aInternational Joint Conference on Artificial Intelligence$$n(31st :$$d2022 :$$cVienna, Austria)
001461920 77608 $$iPrint version:$$aGoebel, Randy$$tTrustworthy Federated Learning$$dCham : Springer International Publishing AG,c2023$$z9783031289958
001461920 830_0 $$aLecture notes in computer science.$$pLecture notes in artificial intelligence.
001461920 830_0 $$aLecture notes in computer science ;$$v13448.
001461920 852__ $$bebk
001461920 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-28996-5$$zOnline Access$$91397441.1
001461920 909CO $$ooai:library.usi.edu:1461920$$pGLOBAL_SET
001461920 980__ $$aBIB
001461920 980__ $$aEBOOK
001461920 982__ $$aEbook
001461920 983__ $$aOnline
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