Federated learning allows a large number of participants to collaboratively train a global model without sharing participant's local data. Participants train local models with their local data and send gradients to the cloud server for aggregation. Unfortunately, as a third party, the cloud server cannot be fully trusted. Existing research has shown that a compromised cloud server can extract sensitive information of participant's local data from gradients. In addition, it can even forge the aggregation result to corrupt the global model without being detected. Therefore, in a secure federated learning system, both the privacy and aggregation correctness of the uploaded gradients should be guaranteed. In this article, we propose a secure and efficient federated learning scheme with verifiable weighted average aggregation. By adopting the masking technique to encrypt both weighted gradients and data size, our scheme can support the privacy-preserving weighted average aggregation of gradients. Moreover, we design the verifiable aggregation tag and propose an efficient verification method to validate the weighted average aggregation result, which greatly improves the performance of the aggregation verification. Security analysis shows that our scheme is provably secure. Extensive experiments demonstrate the efficiency of our scheme compared with the state-of-the-art approaches.

Zhen Yang

Ming Zhou

Haiyang Yu

Richard O. Sinnott

Huan Liu

IEEE Transactions on Network Science and Engineering

拟议的期刊，称为 IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING (TNSE)，致力于及时发表同行评议的技术文章，这些文章涉及网络科学的理论和应用，以及构成系统的元素之间的互连。网络。特别是，IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 发表了关于在基础层面理解、预测和控制网络结构和行为的文章。涵盖的网络类型包括物理或工程网络、信息网络、生物网络、语义网络、经济网络、社交网络和生态网络。该杂志旨在发现管理网络结构、网络功能和网络行为的共同原则，寻求关于理解、预测和控制网络结构和行为的文章。 IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 的另一个跨学科重点是不同类型网络之间的相互作用和共同演化。

The IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING (TNSE) is a journal dedicated to the timely publication of peer-reviewed technical articles that address the theory and applications of network science, as well as the interconnections among the elements that constitute network systems. This journal particularly focuses on research aimed at understanding, predicting, and controlling network structures and behaviors at a fundamental level. The types of networks covered include physical or engineering networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. The journal aims to discover common principles for managing network structures, functions, and behaviors, seeking articles that contribute to the understanding, prediction, and control of network structures and behaviors. Another interdisciplinary focus of the IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING is the interactions and co-evolution between different types of networks.

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING (TNSE) 是一本致力于及时发表同行评审的技术文章的期刊，涵盖网络科学的理论与应用，以及构成网络系统的元素之间的相互连接。该期刊特别关注在基础层面上理解、预测和控制网络结构与行为的研究。涵盖的网络类型包括物理或工程网络、信息网络、生物网络、语义网络、经济网络、社交网络和生态网络。期刊旨在发现管理网络结构、功能和行为的共同原则，并寻求关于理解、预测和控制网络结构与行为的研究文章。IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 还特别关注不同类型网络之间的相互作用与共同演化。

Efficient and Secure Federated Learning With Verifiable Weighted Average Aggregation

This article designs the verifiable aggregation tag and proposes an efficient verification method to validate the weighted average aggregation result, which greatly improves the performance of the aggregation verification and shows that the scheme is provably secure.

Category	Quartile
计算机科学	2
计算机科学, 数学跨学科应用	1
计算机科学, 工程综合	2

Efficient and Secure Federated Learning With Verifiable Weighted Average Aggregation

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Journal Info

Category	Quartile
MATHEMATICS, INTERDISCIPLINARY APPLICATIONS	1