Federated computation: a survey of concepts and challenges

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Publicat a:Distributed and Parallel Databases vol. 42, no. 3 (Sep 2024), p. 299
Autor principal: Bharadwaj, Akash
Altres autors: Cormode, Graham
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Springer Nature B.V.
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Accés en línia:Citation/Abstract
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100 1 |a Bharadwaj, Akash  |u Meta, Menlo Park, USA (GRID:grid.453567.6) (ISNI:0000 0004 0615 529X) 
245 1 |a Federated computation: a survey of concepts and challenges 
260 |b Springer Nature B.V.  |c Sep 2024 
513 |a Journal Article 
520 3 |a Federated Computation is an emerging area that seeks to provide stronger privacy for user data, by performing large scale, distributed computations where the data remains in the hands of users. Only the necessary summary information is shared, and additional security and privacy tools can be employed to provide strong guarantees of secrecy. The most prominent application of federated computation is in training machine learning models (federated learning), but many additional applications are emerging, more broadly relevant to data management and querying data. This survey gives an overview of federated computation models and algorithms. It includes an introduction to security and privacy techniques and guarantees, and shows how they can be applied to solve a variety of distributed computations providing statistics and insights to distributed data. It also discusses the issues that arise when implementing systems to support federated computation, and open problems for future research. 
651 4 |a Europe 
653 |a Data management 
653 |a Data processing 
653 |a Machine learning 
653 |a Personal information 
653 |a Computation 
653 |a Regulation 
653 |a Security 
653 |a Privacy 
653 |a Federated learning 
653 |a Communication 
700 1 |a Cormode, Graham  |u Meta, Coventry, UK (GRID:grid.436437.1) 
773 0 |t Distributed and Parallel Databases  |g vol. 42, no. 3 (Sep 2024), p. 299 
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