An Anonymous Routing Federated Learning Framework for Data Privacy Protection

doi:10.3969/j.issn.1671-1122.2025.03.011

Abstract

Abstract:

Federated learning is a distributed machine learning framework that enables multiple participants to collaboratively train a global model without sharing their training data, thereby effectively protecting client data privacy. However, federated learning still faces risks related to model parameter leakage and identity privacy during communication. To address these issues, an anonymous routing federated learning framework for data privacy protection (SecFL) was designed, aimed at ensuring the secure and trustworthy transmission of model parameters in federated learning. SecFL introduced a novel group-pairing the onion router protocol, which used pairing cryptography to encrypt data layer by layer and incorporated the concept of “groups”, allowing all nodes within a group to decrypt the corresponding layer. This not only ensured the confidentiality and security of messages, but also enhanced system anonymity. Experimental results show that compared to the classic onion router and broadcast anonymous routing anonymous routing systems, SecFL achieves a 100% message delivery rate in a shorter time. The anonymity of the source and destination nodes is improved by up to 3.9% and 1.9%, respectively. The path anonymity can be increased by up to 24.8% when half of the nodes are compromised. Additionally, the SecFL framework demonstrates excellent convergence performance in federated learning.

Key words: federated learning, the onion router, anonymous communication, privacy protection

CLC Number:

TP309

LI Jiadong, ZENG Haitao, PENG Li, WANG Xiaoding. An Anonymous Routing Federated Learning Framework for Data Privacy Protection[J]. Netinfo Security, 2025, 25(3): 494-503.

Figures/Tables 10

References 28

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