Enhancing Edge Computing with Federated Learning and AI

The convergence of edge computing and artificial intelligence (AI) has resulted in the emergence of a paradigm shift referred to as edge AI.

This novel methodology seeks to deliver the computational capabilities of AI directly to edge devices, including but not limited to smartphones, IoT devices, and other interconnected electronics. The integration of AI and edge computing facilitates instantaneous data processing, analysis, and decisionmaking at the network's periphery, presenting a multitude of benefits in comparison to conventional cloudcentric AI frameworks.

Edge AI pertains to the implementation of models and algorithms for artificial intelligence that are executed directly on edge devices or local edge servers. By obviating the necessity to transmit data to a centralised cloud server for processing, edge AI effectively diminishes latency and optimises efficiency. By capitalising on the computational capabilities inherent in the devices, this methodology expedites response times and enhances the overall user experience.

Edge computing in AI

With the increasing need for real-time decision-making and low-latency applications, cutting-edge AI solutions are significantly contributing to the improvement of edge computing services. This article will examine the diverse applications of state-of-the-art AI technologies that aim to enhance and optimise the performance of edge computing services.

There is a growing emphasis among contemporary AI solutions on augmenting the functionalities of edge devices. By ensuring that edge devices can efficiently execute sophisticated algorithms, AI-driven optimisations enable data processing on the device. This feature not only diminishes latency but also empowers devices with limited resources to execute operations that were previously exclusive to more robust servers.