Mahmuda Akter and Nour Moustafa, University of New South Wales Canberra, Australia

The Internet of Things (IoT) forms intelligent systems, such as smart cities and factories, to enhance productivity and provide revolutionary and automated services to end-users and organisations. An IoT ecosystem requires more dynamics and heterogeneity with advanced privacy preservation. Federated Learning (FL) addresses the challenge of maintaining data privacy using a privacy-preserving sharing mechanism instead of transmitting raw data. However, the latest cyber threats cause privacy and security breaches. This study systematically analyses federated learning-based privacy-preserving methods in IoT systems. A standard IoT architecture with possible privacy threats is illustrated. Also, Federated Learning schemes and their taxonomies are discussed in a privacy-preserving manner, with initial experiments proving the significance of FL based privacy preservation in IoT environments. This finds acceptable noise addition in differential privacy by keeping higher testing accuracy in different settings to enhance privacy preservation of federated learning. Various Federated Learning schemes, challenges and future research directions are covered.

Federated Learning, Privacy-Preserving, Internet of Things (IoT), Privacy Threats