Decentralized AI for Edge Devices with Federated Learning in the Internet of Things

As a result of the development of the Internet of Things (IoT), connectivity and applications that are driven by data have entered a new age. Decentralised artificial intelligence with federated learning has evolved as a game-changing concept in this environment, giving answers to urgent challenges like as data privacy, network latency, and resource optimisation. This ecosystem is characterised by constant change. In this research project, we investigate the fundamentals of decentralised artificial intelligence and its consequences for edge devices in IoT networks. The research presented in this paper demonstrates, via a series of tests, that it is possible to strike an appropriate balance between data privacy and model performance by combining decentralised AI with federated learning. The research results provide system designers with helpful information by drawing attention to the trade-offs that exist between the number of devices, the number of communication cycles, and the level of privacy that is afforded. With the help of decentralised artificial intelligence, businesses are able to take advantage of the opportunities presented by the Internet of Things (IoT) without sacrificing the confidentiality of their data or the responsiveness of their real-time applications. This paper provides a cornerstone reference, setting the way for further research that may improve privacy approaches, expand to larger IoT networks, and identify fresh uses for decentralised AI in the dynamic environment of the Internet of Things (IoT).