Scholarlink

<p>This study examines neural cryptography with homomorphic operations as an alternative secure aggregation method for federated learning (FL). It proposes a novel neural cryptographic system supporting homomorphic addition on fixed-point encrypted data, and consisting of three networks, namely (1) an encryption network (Alice), (2) a homomorphic network (HO), and (3) a decryption network (Bob), along with an adversarial Eve network. Using the MNIST dataset, the proposed Neural Homomorphic Operation System (NHOS) is evaluated against a plaintext baseline and the CKKS scheme, a widely used public-key homomorphic encryption method. The results show that the proposed NHOS approach offers a satisfying performance, i.e., 88.10% accuracy, using quantized weights, highlighting its potential as a lightweight alternative to traditional homomorphic encryption in FL.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Secure federated learning via neural cryptography with homomorphic operations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月22日

查看全文
  • Espen Sele,
  • Ferhat Ozgur Catak,
  • Jungwon Seo,
  • Murat Kuzlu

摘要

This study examines neural cryptography with homomorphic operations as an alternative secure aggregation method for federated learning (FL). It proposes a novel neural cryptographic system supporting homomorphic addition on fixed-point encrypted data, and consisting of three networks, namely (1) an encryption network (Alice), (2) a homomorphic network (HO), and (3) a decryption network (Bob), along with an adversarial Eve network. Using the MNIST dataset, the proposed Neural Homomorphic Operation System (NHOS) is evaluated against a plaintext baseline and the CKKS scheme, a widely used public-key homomorphic encryption method. The results show that the proposed NHOS approach offers a satisfying performance, i.e., 88.10% accuracy, using quantized weights, highlighting its potential as a lightweight alternative to traditional homomorphic encryption in FL.