A Comprehensive Review on Enhancing Autonomous Transport with Federated Learning and Artificial Intelligence Integration - G. Sudhakar, Kavin Francis Xavier, Rajeshwari V., Krishnaraj M., Y. Pradeep Kumar, V. Mustafa (International Journal of Intelligent Systems and Applications in Engineering, 2024)
There has been a surprising amount of curiosity about the Internet of Everything (IoE) technologies powered by the sixth generation (6G), such as self-driving automobiles. Federated Learning (FL) in autonomous driving automobiles can open up several intelligent applications. FL offers spread machine learning model development without requiring the transfer of information from the device to a centralized computer; however, it comes with its own set of deployment difficulties, including durability, the safety of the centralized computer, limitations on communication capabilities, and leakage of privacy because unauthorized collection servers can infer confidential data from the devices themselves. The Internet of Vehicles (IOV), which depicts a linked system of automobiles and infrastructure, is one of these devices. IOV becomes an Intelligent Transportation System (ITS) when combined with the latest innovations in computer training and intelligent technology. For effective and privacy-aware automotive social media, researchers provide an autonomously artificial intelligence-based federated learning (AIFL) architecture in which transnational interchange and verification of nearby on-vehicle machine learning (oVML) update models take place. AIFL leverages the blockchain’s agreement structure to provide oVML without the need for any centralized information for training or organization. Simultaneously, self-driving and robotic vehicles now have far higher levels of cognition and independence because of Deep Learning (DL). Issues about information safety and consumer privacy have become an unavoidable study priority during these revolutions in technology. With its intrinsic decentralization of the natural world, FL offers an alternative to secure deep learning at the edge by allowing training on data-isolated islands while only sending modifications to the model. Federated teaching and learning is a major ITS enabler with a plethora of applications and advantages. It is expected to be widely deployed in 6G networks for various reasons and technologies..