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EPPDA: An Efficient Privacy-Preserving Data Aggregation Federated Learning Scheme
Abstract
Federated learning (FL) is a kind of privacy-awaremachine learning, in which the machine learning models are trained on the users’ side and then the model updates are transmitted to the server for aggregating. As the data owners need not upload their data, FL is a privacy-persevering machine learning model. However, FL is weak as it suffers from a reverse attack, in which an adversary can get users’ data by analyzing the user uploaded model. Motivated by this, in this paper, based on the secret sharing, we design, an efficient privacy-preserving data aggregation mechanism for FL, to resist the reverse attack, which can aggregate users’ trained models secretly without leaking the user’s model. Moreover,EPPDAhas efficient fault tolerance for the user disconnection. Even if a large number of users are disconnected when the protocol runs, EPPDA will execute normally. Analysis shows that the EPPDA can provide a sum of locally trained models to the server without leaking any single user’s model. Moreover, adversary can not get any non-public information from the communication channel.Efficiency verificationproves that the EPPDA not only protects users’ privacy but also needs fewer computing and communication resources.
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In the citing paper
“While using FL, each hospital trains their model based on locally available X-ray images and only shares gradients with a global model for computation, hence ensuring privacy preservation [78].”
From Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey (2023) · cites this paper as [78]
In the citing paper
“Similar results have been achieved by EPPDA [69], an efficient privacy-preserving data aggregation method for FL that, based on secret sharing to resist the reverse attack, can covertly aggregate user-trained models without disclosing the user model.”
From Handling privacy-sensitive medical data with federated learning: Challenges and future directions (2023) · cites this paper as [69]
In the citing paper
“Adopting the homomorphisms of secret sharing, it preserves user privacy and requires less computing and communication resources than alternatives as [62], and is able to provide acceptable fault tolerance in the event of user disconnection.”
From Handling privacy-sensitive medical data with federated learning: Challenges and future directions (2023) · cites this paper as [69]
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Metadata
- Authors
- Song, J., Wang, W., Gadekallu, T. R., Cao, J., & Liu, Y.
- Affiliations
- Guilin University of Electronic Technology (CN)
- University of Hong Kong (HK)
- Vellore Institute of Technology University (IN)
- Venue
- IEEE Transactions on Network Science and Engineering
- Published
- 2023-09-01
- Volume
- 10
- Issue
- 5
- Pages
- 3047-3057
- ISSN
- 2327-4697 (ISSN-L)
- 2334-329X
- OpenAlex
- W4214719528
- Cited by
- 150
- References
- 40
- Citations by year
- 2026: 16 · 2025: 35 · 2024: 35 · 2023: 35
- FWCI
- 11.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Network Science and Engineering (publishedVersion)
- Funders
- National Natural Science Foundation of China (62072133, 61662016)
- Guangxi Natural Science Foundation (2018GXNSFDA281040)
- Study Abroad Program for Graduate Student of the Guilin University of Electronic Technology (GDYX2019003)
- New Generation Artificial Intelligence (2020AAA0109300)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Keywords
- federated learning, privacy preservation, data aggregation, secret sharing, model aggregation, fault tolerance, communication efficiency
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