Efficient Grouping Secure Aggregation Method Based on Binary Tree

SUN Yi; ZHOU Chuanxin; WANG Degang; YANG Fan; GAO Qi

doi:10.11999/JEIT220745

Volume 45 Issue 7

Jul. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(7): 2546-2553

SUN Yi, ZHOU Chuanxin, WANG Degang, YANG Fan, GAO Qi. Efficient Grouping Secure Aggregation Method Based on Binary Tree[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2546-2553. doi: 10.11999/JEIT220745

Citation:

SUN Yi, ZHOU Chuanxin, WANG Degang, YANG Fan, GAO Qi. Efficient Grouping Secure Aggregation Method Based on Binary Tree[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2546-2553. doi: 10.11999/JEIT220745

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Efficient Grouping Secure Aggregation Method Based on Binary Tree

doi: 10.11999/JEIT220745

Department of Cryptogram Engineering, Information Engineering University, Zhengzhou 450001, China

Received Date: 2022-06-07
Rev Recd Date: 2022-09-04

Available Online: 2022-09-07

Publish Date: 2023-07-10

Abstract

Abstract

Secure aggregation is a key step to ensure the security and privacy of local model aggregation in federated learning security sharing. However, the existing methods have many problems, such as high computational overhead, poor fairness mechanism, privacy disclosure, and inability to resist quantum attack. Therefore, Tree-Aggregate, an efficient grouping secure aggregation method based on binary tree is proposed in this paper. Firstly, the binary tree based user group security communication protocol can reduce the computation cost from $\textstyle O\left( {N{\text{l}}{{\text{g}}^2}{\text{lg}}N{\text{lglglg}}N} \right)$ to $\textstyle O\left( {{\text{lg}}N{\text{lg}}N} \right)$ magnitude and ensure the fairness of the computation cost through the uniform allocation mechanism. Then, a random padding algorithm is proposed to solve the privacy leakage problem caused by a single user. Finally, the anti-quantum attack capability of tree-aggregate method is improved by incorporating lattice-key exchange protocol. Through theoretical analysis, tree-aggregate can change the growth rate of computation cost from linear level to logarithmic level, and through experimental comparative analysis, when the number of users N ≥300, computation cost is reduced by nearly 15 times compared with existing methods.
- Federated learning,
- Security aggregation,
- Grouping topology,
- Fairness

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References(17)

References

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