Collaborative IoT learning with secure peer-to-peer federated approach

IF 4.5 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Neveen Mohammad Hijazi, Moayad Aloqaily, Mohsen Guizani
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引用次数: 0

Abstract

Federated Learning (FL) has emerged as a powerful model for training collaborative machine learning (ML) models while maintaining the privacy of participants’ data. However, traditional FL methods can exhibit limitations such as increased communication overhead, vulnerability to poisoning attacks, and reliance on a central server, which can impede their practicality in certain IoT applications. In such cases, the necessity of a central server to oversee the learning process may be infeasible, particularly in situations with limited connectivity and energy management. To address these challenges, peer-to-peer FL (P2PFL) offers an alternative approach, providing greater adaptability by enabling participants to collaboratively train their own models alongside their peers. This paper introduces an original framework that combines P2PFL and Homomorphic Encryption (HE), enabling secure computations on encrypted data. Furthermore, a defense approach against poisoning attacks based on cosine similarity is introduced These techniques enable users to collectively learn while preserving data privacy and accounting for ideal energy optimization. The proposed approach has demonstrated superior performance metrics in terms of accuracy, F-scores, and loss when compared to other similar approaches. Furthermore, it offers robust privacy and security measures, leading to an enhanced security level, with improvements ranging from 5.5% to 14.6%. Moreover, we demonstrate that the proposed approach effectively reduces the communication overhead. The proposed approach resulted in impressive reductions in communication overhead ranging from 63.8% to 79.6%. The implementation of these security models was cumbersome, but we have made the code publicly available for your reference 1.

采用安全的点对点联盟方式进行物联网协作学习
联盟学习(FL)已成为训练协作式机器学习(ML)模型的一种强大模式,同时还能维护参与者的数据隐私。然而,传统的联合学习方法可能会表现出一些局限性,例如通信开销增加、易受中毒攻击以及依赖中央服务器,这可能会阻碍其在某些物联网应用中的实用性。在这种情况下,需要中央服务器来监督学习过程可能是不可行的,尤其是在连接和能源管理有限的情况下。为应对这些挑战,点对点 FL(P2PFL)提供了另一种方法,通过让参与者与同伴一起协作训练自己的模型,提供更强的适应性。本文介绍了一个将 P2PFL 和同态加密(HE)相结合的原创框架,从而实现对加密数据的安全计算。此外,本文还介绍了一种基于余弦相似性的中毒攻击防御方法。这些技术使用户能够在集体学习的同时保护数据隐私并实现理想的能量优化。与其他类似方法相比,所提出的方法在准确性、F 分数和损失方面都表现出了卓越的性能指标。此外,它还提供了强大的隐私和安全措施,从而提高了安全级别,改进幅度从 5.5% 到 14.6%。此外,我们还证明了所提出的方法能有效减少通信开销。所提出的方法显著降低了 63.8% 到 79.6% 的通信开销。这些安全模型的实现过程非常繁琐,但我们已经公开了代码,供大家参考1。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computer Communications
Computer Communications 工程技术-电信学
CiteScore
14.10
自引率
5.00%
发文量
397
审稿时长
66 days
期刊介绍: Computer and Communications networks are key infrastructures of the information society with high socio-economic value as they contribute to the correct operations of many critical services (from healthcare to finance and transportation). Internet is the core of today''s computer-communication infrastructures. This has transformed the Internet, from a robust network for data transfer between computers, to a global, content-rich, communication and information system where contents are increasingly generated by the users, and distributed according to human social relations. Next-generation network technologies, architectures and protocols are therefore required to overcome the limitations of the legacy Internet and add new capabilities and services. The future Internet should be ubiquitous, secure, resilient, and closer to human communication paradigms. Computer Communications is a peer-reviewed international journal that publishes high-quality scientific articles (both theory and practice) and survey papers covering all aspects of future computer communication networks (on all layers, except the physical layer), with a special attention to the evolution of the Internet architecture, protocols, services, and applications.
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