Federated Learning for Intelligent IoT Systems: Background, Frameworks, and Optimization Techniques
Partha Pratim Ray · 2024
In the rapidly expanding realm of the Intelligent Internet of Things (ITIoT) and federated learning, ensuring optimal performance while maintaining stringent security and privacy standards is paramount. This chapter first delves into the background of federate learning and it's necessary for existing IoT systems to emerge as intelligent. Second, we provide a comparative analysis of the state-of-the-art industrial framework for leveraging ITIoT applications and development. Third, we delve deep into the optimization techniques for federated ITIoT systems, highlighting key strategies for efficient communication, model compression, asynchronous updates, and staleness handling. Moreover, the significance of robust privacy-preserving mechanisms is underlined, with a comprehensive exploration of potential attack vectors and their respective mitigation strategies in federated IoT setups. Through a multifaceted approach, this chapter provides an in-depth understanding of the challenges and solutions associated with federated ITIoT systems.