From distributed machine learning to federated learning: In the view of data privacy and security

Article


Shen, Sheng, Zhu, Tianqing, Wu, Di, Wang, Wei and Zhou, Wanlei. 2022. "From distributed machine learning to federated learning: In the view of data privacy and security." Concurrency and Computation: Practice and Experience. 34 (16). https://doi.org/10.1002/cpe.6002
Article Title

From distributed machine learning to federated learning: In the view of data privacy and security

ERA Journal ID17819
Article CategoryArticle
AuthorsShen, Sheng, Zhu, Tianqing, Wu, Di, Wang, Wei and Zhou, Wanlei
Journal TitleConcurrency and Computation: Practice and Experience
Journal Citation34 (16)
Article Numbere6002
Number of Pages19
Year2022
PublisherJohn Wiley & Sons
Place of PublicationUnited Kingdom
ISSN1532-0626
1532-0634
Digital Object Identifier (DOI)https://doi.org/10.1002/cpe.6002
Web Address (URL)https://onlinelibrary.wiley.com/doi/full/10.1002/cpe.6002
Abstract

Federated learning is an improved version of distributed machine learning that further offloads operations which would usually be performed by a central server. The server becomes more like an assistant coordinating clients to work together rather than micromanaging the workforce as in traditional DML. One of the greatest advantages of federated learning is the additional privacy and security guarantees it affords. Federated learning architecture relies on smart devices, such as smartphones and IoT sensors, that collect and process their own data, so sensitive information never has to leave the client device. Rather, clients train a submodel locally and send an encrypted update to the central server for aggregation into the global model. These strong privacy guarantees make federated learning an attractive choice in a world where data breaches and information theft are common and serious threats. This survey outlines the landscape and latest developments in data privacy and security for federated learning. We identify the different mechanisms used to provide privacy and security, such as differential privacy, secure multiparty computation and secure aggregation. We also survey the current attack models, identifying the areas of vulnerability and the strategies adversaries use to penetrate federated systems. The survey concludes with a discussion on the open challenges and potential directions of future work in this increasingly popular learning paradigm.

Keywordsdata privacy; security; federated learning; distributed machine learning
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204602. Artificial intelligence
4604. Cybersecurity and privacy
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Byline AffiliationsUniversity of Technology Sydney
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