Secure federated learning at the edge

联邦学习（FL）是一种基本的机器学习（ML）模型，它支持具有隐私保护特征的分布式模型训练。自b谷歌AI于2017年成立以来，它被用于不同的垂直领域，如电子医疗、智能电网、工业部门、保险部门、自动驾驶汽车、金融科技等。与传统ML模型相比，FL的一些特征优势包括数据隐私和多样性，即使在低/无互联网连接的情况下也能实时分析数据，硬件效率等。尽管有这些优势，但FL很容易受到不同的网络攻击，如数据和模型中毒、推理、后门、恶意服务器、通信瓶颈等。因此，作为本研究项目的一部分，我们建议开发一个高效、安全和隐私敏感的FL框架，可以使用边缘计算范式跨不同的应用领域进行利用。本研究将利用差分隐私、同态加密和区块链的优势；设计的框架将在实时和基准数据集上进行验证，以提高有效性。这项研究将允许总部位于加拿大的公司Cistech Ltd为其客户开发更强大、更安全、更保护隐私的基于fl的解决方案，特别关注不断爆发的不同攻击向量，随着ML模型的复杂性和复杂性的增加。因此，该公司将能够确保为其客户部署安全的基于fl的解决方案。研究过程中设计的解决方案将被转移到工业中，并可以部署在他们即将开展的跨不同领域的项目中。

Federated Learning (FL) is emerging as one of the fundamental Machine Learning (ML) models that supports distributed model training with privacy-preserving characteristics. Ever since its inception in 2017 by Google AI, it is being utilized across different verticals such as e-healthcare, smart grids, industrial sector, insurance sector, autonomous vehicles, FinTech, etc. Some of the characteristic advantages of FL against the traditional ML models include data privacy and diversity, real-time data analysis even during low/no Internet connectivity, hardware efficiency, etc. Despite these advantages, FL is founded vulnerable to different cyberattacks such as data and model poisoning, inferences, backdoors, malicious server, communication bottlenecks, etc.Thus, as part of this research project, we propose to develop an efficient, secure, and privacy-aware FL framework that can be leveraged across different application domains using the Edge computing paradigm. The research will employee the advantages of differential privacy, homomorphic encryption, and blockchain; and the designed framework will be validated on real-time and benchmark datasets for enhanced efficacy. This research will allow the Canada-based company Cistech Ltd to develop more robust, secure, and privacy-preserving FL-based solutions for its client, paying special attention to the on-going outbreak of different attack vectors on the ML models with increasing complexity and sophistication. Thus, the company will be able to ensure the deployment of secure FL-based solutions to it clients. The designed solution during this research will be transferred to industry and can be deployed in their upcoming projects across different domains.