Collaborative Artificial Intelligence (AI) for Diverse Internet of Things (IoT) Devices

Automotive Internet of Things (IoT) presents a myriad of possibilities for addressing challenges within the automobile industry. By facilitating connectivity to the internet, it establishes a network for seamless data exchange, communication, enhanced safety measures, advanced infotainment options, and robust solutions for privacy and security concerns. This interconnected automotive ecosystem opens up new avenues for innovation and efficiency, paving the way for a more interconnected and technologically advanced future in the automotive sector. Automotive IoT, an extension of vehicular ad hoc networks (VANET) with internet connectivity, leverages components like On-Board Units (OBUs) and Roadside Units (RSUs) to revolutionize the automotive industry. It addresses challenges in connected vehicles, telematics, communication, safety, infotainment, and predictive maintenance, contributing to a more interconnected and technologically advanced automotive ecosystem. It plays a crucial role in addressing and enhancing the following key aspects. The integration of automotive IoT in vehicles brings about several privacy and security challenges which includes but not limited to data privacy, unauthorized hacking, theft identification, vehicle tracking, etc. The privacy often stems from the prevalent practice of centralized data exchange and communication with the cloud in the current architectural setup With Vehicle-to-Infrastructure (V2I) communication the types of data generated varies across devices in the automotive ecosystem. To address these issues the federated learning (FL) allows participants to collaboratively train the local models on local data without revealing their sensitive information to the central cloud server by offloading the training on edge servers.