Secure federated learning at the edge

确保边缘联合学习的安全

• 批准号: 568539-2021
• 负责人: Agarwal, Anjali
• 金额: $ 2.19万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=723193
• 关键词: Secure; federated; learning; edge

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.

联邦学习（FL）正在成为支持具有隐私保护特性的分布式模型训练的基本机器学习（ML）模型之一。自2017年由Google AI推出以来，它已被用于不同的垂直行业，如电子医疗保健，智能电网，工业部门，保险部门，自动驾驶汽车，金融科技等FL相对于传统ML模型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，硬件效率等情况下的实时数据分析。FL被发现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，隐私意识FL框架，可以利用不同的应用领域使用边缘计算范式。该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，以提高效率。这项研究将使总部位于加拿大的公司CiscoTech Ltd能够为其客户开发更强大，更安全，更隐私保护的基于FL的解决方案，特别关注ML模型上不断爆发的不同攻击向量，这些攻击向量越来越复杂和复杂。因此，该公司将能够确保向其客户部署安全的基于FL的解决方案。在此研究期间设计的解决方案将被转移到行业，并可以在不同领域的即将到来的项目中部署。