Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164430
Title: Privacy-preserving aggregation in federated learning: a survey
Authors: Liu, Ziyao
Guo, Jiale
Yang, Wenzhuo
Fan, Jiani
Lam, Kwok-Yan
Zhao, Jun
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Liu, Z., Guo, J., Yang, W., Fan, J., Lam, K. & Zhao, J. (2022). Privacy-preserving aggregation in federated learning: a survey. IEEE Transactions On Big Data. https://dx.doi.org/10.1109/TBDATA.2022.3190835
Project: RG24/20 
RG97/20 
MOE2019-T2-1-176 
Journal: IEEE Transactions on Big Data 
Abstract: Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms and growing concerns over personal data privacy, Privacy-Preserving Federated Learning (PPFL) has attracted tremendous attention from both academia and industry. Practical PPFL typically allows multiple participants to individually train their machine learning models, which are then aggregated to construct a global model in a privacy-preserving manner. As such, Privacy-Preserving Aggregation (PPAgg) as the key protocol in PPFL has received substantial research interest. This survey aims to fill the gap between a large number of studies on PPFL, where PPAgg is adopted to provide a privacy guarantee, and the lack of a comprehensive survey on the PPAgg protocols applied in FL systems. This survey reviews the PPAgg protocols proposed to address privacy and security issues in FL systems. The focus is placed on the construction of PPAgg protocols with an extensive analysis of the advantages and disadvantages of these selected PPAgg protocols and solutions. Additionally, we discuss the open-source FL frameworks that support PPAgg. Finally, we highlight significant challenges and future research directions for applying PPAgg to FL systems and the combination of PPAgg with other technologies for further security improvement.
URI: https://hdl.handle.net/10356/164430
ISSN: 2332-7790
DOI: 10.1109/TBDATA.2022.3190835
Schools: School of Computer Science and Engineering 
Rights: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TBDATA.2022.3190835.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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