A Review of Research on Secure Aggregation for Federated Learning
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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/17/7/308 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850078290207834112 |
|---|---|
| author | Xing Zhang Yuexiang Luo Tianning Li |
| author_facet | Xing Zhang Yuexiang Luo Tianning Li |
| author_sort | Xing Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d412dccbe1fb492b9109774dc4656521 |
| institution | DOAJ |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-d412dccbe1fb492b9109774dc46565212025-08-20T02:45:34ZengMDPI AGFuture Internet1999-59032025-07-0117730810.3390/fi17070308A Review of Research on Secure Aggregation for Federated LearningXing Zhang0Yuexiang Luo1Tianning Li2School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, ChinaFederated 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.https://www.mdpi.com/1999-5903/17/7/308privacy protectionsecure aggregationfederated learning |
| spellingShingle | Xing Zhang Yuexiang Luo Tianning Li A Review of Research on Secure Aggregation for Federated Learning Future Internet privacy protection secure aggregation federated learning |
| title | A Review of Research on Secure Aggregation for Federated Learning |
| title_full | A Review of Research on Secure Aggregation for Federated Learning |
| title_fullStr | A Review of Research on Secure Aggregation for Federated Learning |
| title_full_unstemmed | A Review of Research on Secure Aggregation for Federated Learning |
| title_short | A Review of Research on Secure Aggregation for Federated Learning |
| title_sort | review of research on secure aggregation for federated learning |
| topic | privacy protection secure aggregation federated learning |
| url | https://www.mdpi.com/1999-5903/17/7/308 |
| work_keys_str_mv | AT xingzhang areviewofresearchonsecureaggregationforfederatedlearning AT yuexiangluo areviewofresearchonsecureaggregationforfederatedlearning AT tianningli areviewofresearchonsecureaggregationforfederatedlearning AT xingzhang reviewofresearchonsecureaggregationforfederatedlearning AT yuexiangluo reviewofresearchonsecureaggregationforfederatedlearning AT tianningli reviewofresearchonsecureaggregationforfederatedlearning |