Guest Editorial Split Learning in Consumer Electronics for Smart Cities: Theories, Tools, Applications and Challenges

In the present fast-moving society, the Internet of Things (IoT) is transforming the way services are used in different industries. While it has many benefits, there are also considerable obstacles, especially in the areas of computing power, safety, and handling data. With the continuous evolution and importance of consumer electronics (CE) in smart cities, there is an increasing demand for sustainable and effective solutions to deal with challenges such as widespread sensing, advanced computing, prediction, monitoring, and data sharing. The artificial intelligence (AI) has emerged as a crucial component in the IoT environment, highlighting the need for energy-efficient CE in urban areas. The state of art methods are required to maximize resource usage and maintain high-quality services for smart systems in healthcare, transportation, AI-powered sensing (AIeS), and sustainable networks. The split learning is a technique for distributed deep learning, shows great potential as a solution for these CE applications. It can greatly reduce numerous obstacles linked with intelligent services in smart cities. The split learning enables the training of deep neural networks or split neural networks (SplitNN) using AIeS on various data sources. This method enables the secure and efficient processing of data without the requirement of directly sharing raw labeled data, which is crucial in industries like healthcare, finance, security, and surveillance where data privacy and security are vital. This guest editorial discusses and presents split learning methods in CE applications for smart cities. Using split learning, researchers and developers can develop creative solutions to address resource efficiency, data security, and service quality issues across different smart city sectors as presented further. As the IoT grows and changes, incorporating split learning into CE applications influences the platform for future smart cities.