<br>G-VCFL：分组可验证链式隐私保护联邦学习,IEEE Transactions on Network and Service Management

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G-VCFL：分组可验证链式隐私保护联邦学习
IEEE Transactions on Network and Service Management ( IF 4.7 ) Pub Date : 8-4-2022 , DOI: 10.1109/tnsm.2022.3196404
Zhuangzhuang Zhang ₁ , Libing Wu ₁ , Debiao He ₂ , Qian Wang ₁ , Dan Wu ₃ , Xiaochuan Shi ₁ , Chao Ma ₁

Affiliation

联邦学习作为一种典型的分布式学习范式，在工业物联网、智能家居、智慧城市等领域展现出巨大的潜力，它能够在数据不离开本地用户的情况下实现协作学习。尽管带来了巨大的好处，但它仍然面临着隐私泄露和聚合服务器单点故障的风险。攻击者可以使用中间模型来推断用户隐私，甚至可以通过操纵聚合服务器返回错误的全局模型。为了解决这些问题，人们提出了几种注重隐私保护和安全的联邦学习解决方案。然而，这些解决方案在资源有限的场景中仍然面临挑战。在本文中，我们提出了 G-VCFL，一种分组可验证的链式隐私保护联邦学习方案。具体来说，我们首先使用分组链学习机制来保证用户的隐私，然后提出可验证的安全聚合协议来保证全局模型的可验证性。 G-VCFL 不需要任何复杂的加密原语，也不会引入噪声，而是通过利用轻量级伪随机生成器来实现可验证的隐私保护联合学习。我们通过将 G-VCFL 与其他最先进的方法进行比较，对现实数据集进行了广泛的实验。实验结果和功能评估表明，G-VCFL 在六个实验案例中都是有效的，并且满足所有预期的设计目标。

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G-VCFL: Grouped Verifiable Chained Privacy-Preserving Federated Learning

Federated learning, as a typical distributed learning paradigm, shows great potential in Industrial Internet of Things, Smart Home, Smart City, etc. It enables collaborative learning without data leaving local users. Despite the huge benefits, it still faces the risk of privacy breaches and a single point of failure for aggregation server. Adversaries can use intermediate models to infer user privacy, or even return incorrect global model by manipulating the aggregation server. To address these issues, several federated learning solutions focusing on privacy-preserving and security have been proposed. However, theses solutions still faces challenges in resource-limited scenarios. In this paper, we propose G-VCFL, a grouped verifiable chained privacy-preserving federated learning scheme. Specifically, we first use the grouped chain learning mechanism to guarantee the privacy of users, and then propose a verifiable secure aggregation protocol to guarantee the verifiability of the global model. G-VCFL does not require any complex cryptographic primitives and does not introduce noise, but enables verifiable privacy-preserving federated learning by utilizing lightweight pseudorandom generators. We conduct extensive experiments on real-world datasets by comparing G-VCFL with other state-of-the-art approaches. The experimental results and functional evaluation indicate that G-VCFL is efficient in the six experimental cases and satisfies all the intended design goals.

更新日期：2024-08-26

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