Accountable and Verifiable Secure Aggregation for Federated Learning in IoT Networks

Authors

Xiaoyi Yang, Hong Kong Polytechnic University
Yanqi Zhao, Xi'an Institute of Posts and Telecommunications
Qian Chen, Xidian University
Yong Yu, Shaanxi Normal University
Xiaojiang Du, Stevens Institute of Technology
Mohsen Guizani, Mohamed Bin Zayed University of Artificial IntelligenceFollow

Document Type

Article

Publication Title

IEEE Network

Abstract

In the Internet of things (IoT) networks, largescale IoT devices are connected to the Internet to collect users' data. As a distributed machine learning paradigm, federated learning (FL) collaboratively trains the global model by utilizing large-scale distributed devices, while protecting the privacy of the local data sets of each participant. Federated learning with secure aggregation employs an aggregation server (aggregator) to compute a multiparty sum of model parameter updates of each participants in a secure manner and further realizes the updates. However, existing schemes are usually based on semi-honest assumptions, which make them vulnerable to malicious clients. In addition, they address the random client dropouts problem by increasing the data size, which brings a large communication overhead. To solve these issues, we propose an accountable and verifiable secure aggregation for federated learning framework. Specifically, we employ an SMC protocol based on homomorphic proxy re-authenticators and homomorphic proxy re-encryption to execute secure aggregation, while integrating the blockchain to realize the function of penalty for malicious behavior. Our framework can guarantee the verifiability of data provenance and is accountable for malicious clients. To demonstrate the usability of our framework, we evaluate the specific cryptography schemes and develop a blockchain-based prototype system by using solidity language to test the performance of the framework.

First Page

173

Last Page

179

DOI

10.1109/MNET.001.2200214

Publication Date

11-25-2022

Keywords

Blockchain, Cryptography, Learning systems, Network security

Comments

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Recommended Citation

X. Yang, Y. Zhao, Q. Chen, Y. Yu, X. Du and M. Guizani, "Accountable and Verifiable Secure Aggregation for Federated Learning in IoT Networks," in IEEE Network, vol. 36, no. 5, pp. 173-179, September/October 2022, doi: 10.1109/MNET.001.2200214.