| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
|
| Descrizione fisica |
1 online resource (vi, 534 pages) : illustrations
|
| Disciplina |
006.31
|
| Soggetto topico |
Artificial intelligence
Machine learning
Artificial Intelligence
Machine Learning
|
| ISBN |
3-030-96896-0
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| Nota di contenuto |
Introduction to Federated Learning -- Tree-Based Models for Federated Learning Systems -- Semantic Vectorization: Text and Graph-Based Models -- Personalization in Federated Learning -- Personalized, Robust Federated Learning with Fed+ -- Communication-Efficient Distributed Optimization Algorithms -- Communication-Efficient Model Fusion -- Federated Learning and Fairness -- Introduction to Federated Learning Systems -- Local Training and Scalability of Federated Learning Systems -- Straggler Management -- Systems Bias in Federated Learning -- Protecting Against Data Leakage in Federated Learning: What Approach Should You Choose? -- Private Parameter Aggregation for Federated Learning -- Data Leakage in Federated Learning -- Security and Robustness in Federated Machine Learning -- Dealing with Byzantine Threats to Neural Networks -- Privacy-Preserving Vertical Federated Learning -- Split Learning: A Resource Efficient Model & Data Parallel Approach for Distributed Deep Learning -- Federated Learning for Collaborative Financial Crimes Detection -- Federated Reinforcement Learning for Portfolio Management -- Application of Federated Learning in Medical Imaging -- Advancing Healthcare Solutions with Federated Learning -- A Privacy-preserving Product Recommender System -- Application of Federated Learning in Telecommunications and Edge Computing.
|
| Record Nr. | UNINA-9910584601303321 |
Info
Federated learning : a comprehensive overview of methods and applications / / Heiko Ludwig, Nathalie Baracaldo (editors)