The concept of Federated Learning (FL) has emerged as a convergence of machine learning, information, and communication technology. It is vital to the development of machine learning, which is expected to be fully decentralized, privacy-preserving, secure, and robust. However, general federated learning settings with a central server can't meet requirements in decentralized environment. In this paper, we propose a decentralized, secure and privacy-preserving global model training protocol, named PPT, for federated learning in Peer-to-peer (P2P) Networks. PPT uses a one-hop communication form to aggregate local model update parameters and adopts the symmetric cryptosystem to ensure security. It is worth mentioning that PPT modifies the Eschenauer-Gligor (E-G) scheme to distribute keys for encryption. In terms of privacy preservation, PPT generates random noise to disturb local model update parameters. The noise is eliminated ultimately, which ensures the global model performance compared with other noise-based privacy-preserving methods in FL, e.g., differential privacy. PPT also adopts Game Theory to resist collusion attacks. Through extensive analysis, we demonstrate that PPT various security threats and preserve user privacy. Ingenious experiments demonstrate the utility and efficiency as well.
翻译:联邦学习联合会(FL)的概念已成为机器学习、信息和通信技术的趋同,对于发展机器学习至关重要,而机器学习预期是完全分散的、隐私保护的、安全的和稳健的。然而,具有中央服务器的一般联邦学习环境不能满足分散环境的要求。在本文中,我们提议了一个分散的、安全的和隐私保护的全球示范培训协议,名为PPPT,用于同侪网络(P2P)的联结学习。PPPT使用一种一手式通信表格来综合当地模式更新参数,并采用对称密码系统以确保安全。值得一提的是,PPT修改了Eschenauer-Gligor(E-G)计划来分配加密钥匙。在保护隐私方面,PPT产生随机的噪音来扰乱当地模式更新参数。噪音最终被消除,这确保全球模型的性能与其他基于噪音的隐私保护方法相比,例如,差异隐私,PPPTOT还采用游戏理论来抵制串联攻击。通过广泛的分析来保持用户安全性。我们展示了各种保密性。