A Review of Research on Secure Aggregation for Federated Learning
Xing Zhang,
Yuexiang Luo and
Tianning Li ()
Additional contact information
Xing Zhang: School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China
Yuexiang Luo: School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China
Tianning Li: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Future Internet, 2025, vol. 17, issue 7, 1-39
Abstract:
Federated learning (FL) is an advanced distributed machine learning method that effectively solves the data silo problem. With the increasing popularity of federated learning and the growing importance of privacy protection, federated learning methods that can securely aggregate models have received widespread attention. Federated learning enables clients to train models locally and share their model updates with the server. While this approach allows collaborative model training without exposing raw data, it still risks leaking sensitive information. To enhance privacy protection in federated learning, secure aggregation is considered a key enabling technology that requires further in-depth investigation. This paper summarizes the definition, classification, and applications of federated learning; reviews secure aggregation protocols proposed to address privacy and security issues in federated learning; extensively analyzes the selected protocols; and concludes by highlighting the significant challenges and future research directions in applying secure aggregation in federated learning. The purpose of this paper is to review and analyze prior research, evaluate the advantages and disadvantages of various secure aggregation schemes, and propose potential future research directions. This work aims to serve as a valuable reference for researchers studying secure aggregation in federated learning.
Keywords: privacy protection; secure aggregation; federated learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
https://www.mdpi.com/1999-5903/17/7/308/pdf (application/pdf)
https://www.mdpi.com/1999-5903/17/7/308/ (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:17:y:2025:i:7:p:308-:d:1703826
Access Statistics for this article
Future Internet is currently edited by Ms. Grace You
More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().