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.

联合学习（FL）正在成为基本的机器学习（ML）模型之一，该模型支持具有隐私性特征的分布式模型培训。自Google AI于2017年成立以来，它一直在不同的垂直领域（例如E-Healthcare，智能电网，工业部门，保险行业，自动驾驶汽车，金融科技等）中使用。模型包括数据隐私和多样性，即使在低/无互联网连接，硬件效率等期间，实时数据分析。通信瓶颈等。作为该研究项目的一部分，我们建议开发一个有效，安全和隐私感知的FL框架，该框架可以使用边缘计算范式在不同的应用程序域中利用，该框架可以利用。该研究将雇用差异隐私，同态加密和区块链的优势；并且设计的框架将在实时和基准数据集上进行验证，以增强功效。这项研究将使总部位于加拿大的公司CISTECH LTD能够为客户开发更健壮，安全和隐私的基于FL和精致。因此，该公司将能够确保向IT客户端部署安全的解决方案。这项研究期间设计的解决方案将转移到行业，并可以在其即将到来的不同领域的项目中部署。