Secure federated learning at the edge

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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.