Federated learning : privacy and incentive / / edited by Qiang Yang, Lixin Fan, and Han Yu |
Edizione | [1st ed. 2020.] |
Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2020] |
Descrizione fisica | 1 online resource (X, 286 p. 94 illus., 82 illus. in color.) |
Disciplina | 006.31 |
Collana | Lecture Notes in Artificial Intelligence |
Soggetto topico |
Federated database systems
Application software Machine learning |
ISBN | 3-030-63076-5 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Privacy -- Threats to Federated Learning -- Rethinking Gradients Safety in Federated Learning -- Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks -- Task-Agnostic Privacy-Preserving Representation Learning via Federated Learning -- Large-Scale Kernel Method for Vertical Federated Learning -- Towards Byzantine-resilient Federated Learning via Group-wise Robust Aggregation -- Federated Soft Gradient Boosting Machine for Streaming Data -- Dealing with Label Quality Disparity In Federated Learning -- Incentive -- FedCoin: A Peer-to-Peer Payment System for Federated Learning -- Efficient and Fair Data Valuation for Horizontal Federated Learning -- A Principled Approach to Data Valuation for Federated Learning -- A Gamified Research Tool for Incentive Mechanism Design in Federated Learning -- Budget-bounded Incentives for Federated Learning -- Collaborative Fairness in Federated Learning -- A Game-Theoretic Framework for Incentive Mechanism Design in Federated Learning -- Applications -- Federated Recommendation Systems -- Federated Learning for Open Banking -- Building ICU In-hospital Mortality Prediction Model with Federated Learning -- Privacy-preserving Stacking with Application to Cross-organizational Diabetes Prediction. . |
Record Nr. | UNINA-9910427668503321 |
Cham, Switzerland : , : Springer, , [2020] | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Federated learning : privacy and incentive / / edited by Qiang Yang, Lixin Fan, and Han Yu |
Edizione | [1st ed. 2020.] |
Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2020] |
Descrizione fisica | 1 online resource (X, 286 p. 94 illus., 82 illus. in color.) |
Disciplina | 006.31 |
Collana | Lecture Notes in Artificial Intelligence |
Soggetto topico |
Federated database systems
Application software Machine learning |
ISBN | 3-030-63076-5 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Privacy -- Threats to Federated Learning -- Rethinking Gradients Safety in Federated Learning -- Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks -- Task-Agnostic Privacy-Preserving Representation Learning via Federated Learning -- Large-Scale Kernel Method for Vertical Federated Learning -- Towards Byzantine-resilient Federated Learning via Group-wise Robust Aggregation -- Federated Soft Gradient Boosting Machine for Streaming Data -- Dealing with Label Quality Disparity In Federated Learning -- Incentive -- FedCoin: A Peer-to-Peer Payment System for Federated Learning -- Efficient and Fair Data Valuation for Horizontal Federated Learning -- A Principled Approach to Data Valuation for Federated Learning -- A Gamified Research Tool for Incentive Mechanism Design in Federated Learning -- Budget-bounded Incentives for Federated Learning -- Collaborative Fairness in Federated Learning -- A Game-Theoretic Framework for Incentive Mechanism Design in Federated Learning -- Applications -- Federated Recommendation Systems -- Federated Learning for Open Banking -- Building ICU In-hospital Mortality Prediction Model with Federated Learning -- Privacy-preserving Stacking with Application to Cross-organizational Diabetes Prediction. . |
Record Nr. | UNISA-996418217803316 |
Cham, Switzerland : , : Springer, , [2020] | ||
Materiale a stampa | ||
Lo trovi qui: Univ. di Salerno | ||
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