000			04932nam a22003735i 4500
001			394692
003			ES-MaUEC
005			20230102123151.0
007			cr nn 008mamaa
008			220707s2022 sz | s |||| 0|eng d
020			_a9783030968960
024	7		_a10.1007/978-3-030-96896-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
245	1	0	_aFederated Learning _bA Comprehensive Overview of Methods and Applications _cedited by Heiko Ludwig, Nathalie Baracaldo
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (VI, 534 páginas) _b134 ilustraciones, 117 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
505	0		_aIntroduction 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.
520			_aFederated Learning: A Comprehensive Overview of Methods and Applications presents an in-depth discussion of the most important issues and approaches to federated learning for researchers and practitioners. Federated Learning (FL) is an approach to machine learning in which the training data are not managed centrally. Data are retained by data parties that participate in the FL process and are not shared with any other entity. This makes FL an increasingly popular solution for machine learning tasks for which bringing data together in a centralized repository is problematic, either for privacy, regulatory or practical reasons. This book explains recent progress in research and the state-of-the-art development of Federated Learning (FL), from the initial conception of the field to first applications and commercial use. To obtain this broad and deep overview, leading researchers address the different perspectives of federated learning: the core machine learning perspective, privacy and security, distributed systems, and specific application domains. Readers learn about the challenges faced in each of these areas, how they are interconnected, and how they are solved by state-of-the-art methods. Following an overview on federated learning basics in the introduction, over the following 24 chapters, the reader will dive deeply into various topics. The first part addresses algorithmic questions of solving different machine learning tasks in a federated way and how to train efficiently, at scale, and fairly. Another part focuses on providing clarity on how to select privacy and security solutions in a way that can be tailored to specific use cases, while another considers the pragmatics of the systems where the federated learning process will run. The book also covers other important use cases for federated learning, such as split learning and vertical federated learning. Finally, the book includes some chapters focusing on applying FL in real-world enterprise settings.
700	1		_aLudwig, Heiko _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aBaracaldo, Nathalie _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783030968953
776	0	8	_iPrinted edition: _z9783030968977
776	0	8	_iPrinted edition: _z9783030968984
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-96896-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aSpringer_Computer_2022
999			_c394692 _d394692