An adaptive ant colony optimisation for improved lane detection in intelligent automobile vehicles

Salawudeen, Ahmed Tijani; Umoh, Ime J.; Sadiq, Bashir Olaniyi; Oyenike, Olubukola Ishola; Mu', Muhammed Bashir; azu, N.A.

doi:10.1504/ijbic.2022.121225

Cited by 4 publications

(1 citation statement)

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“…As the information technology rapid evolves, the functions of personal mobile devices have gradually become intelligent, which provides an effective guarantee for processing distributed edge data safely in FL [138]. The combination of edge computing and FL enables edge devices to train a common machine learning model with superior performance without sending source data to cloud devices [139].…”

Section: Federated Learning In Edge Computingmentioning

confidence: 99%

A survey on federated learning: challenges and applications

Zhang

Lan

et al. 2022

Int. J. Mach. Learn. & Cyber.

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y impact on human life [1][2][3]. As great progress has been made in AI technology in recent years, various application fields have stepped into intelligence [4,5]. On the road of AI development, models, computing power, chip performance, and other technical issues have been the focus of academic research, so that AI technology can continue to evolve. For machines to truly approach the level of human thought, they need to be trained with vast amounts of real data [6,7]. However, cloud computing power, data security, data silos and other risks will inevitably become constraints for AI to win user trust, collect private data, and achieve largescale implementation [8]. Therefore, it is an urgent need for a practical and effective technique to alleviate the above problems and make the AI full of vitality again. Under this background, the concept of "federated learning (FL) " came into being.The notion of FL is first proposed by Google in 2016, mainly to make android mobile phone users update models locally without revealing private personal data [9]. After then, Google implemented an application-oriented FL system. The designed FL system, which focused on running federated average (FedAvg) algorithms on mobile phones, can perform federated analytics and be applied to monitor statistics for large-scale cluster equipment without recording raw device data to the cloud server. FL is one of the most Zhihua Cui

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Section: Federated Learning In Edge Computingmentioning

confidence: 99%