Cite this Article

Mrs.D. Jayasree, D. Deepika, N. Prabha devi, 2025. "Fusion of Edge AI and Federated Learning in Smart Cities", International Journal of Research in Artificial Intelligence and Data Science(IJRAIDS)1(2): 10-21.

The International Journal of Research in Artificial Intelligence and Data Science (IJRAIDS)
© 2025 by IJRAIDS
Volume 1 Issue 2
Year of Publication : 2025
Authors : Mrs.D. Jayasree, D. Deepika, N. Prabha devi
Doi : XXXX XXXX XXXX

Keywords

Edge AI, federated learning, smart cities, privacy protection, decentralized intelligence, real-time processing, and city infrastructure.

Abstract

The emergence of smart cities has necessitated the development of data processing systems that are real-time, decentralized, and privacy-conscious. This paper analyzes the integration of Edge Artificial Intelligence (Edge AI) with Federated Learning (FL) as an innovative approach to enhance data management, security, and decision-making in intelligent urban environments. Edge AI enables localized data analysis with reduced latency, whilst Federated Learning safeguards data privacy by facilitating model training without necessitating centralized data storage. This combination fixes big difficulties with scalability, latency, and privacy. But there are a lot of challenges that make it challenging to employ this integration in real life. For example, devices aren't all the same, data isn't all the same, communication is slow, and coordinating in real time is hard. Also, limitations including limited edge resources, privacy-utility trade-offs, and concerns about how well models can be comprehended might make smart city services less effective and less obvious. The paper examines the design, applications, advantages, challenges, and prospective developments of this integration within the context of smart cities. The growth of smart cities has made it very important to have data processing systems that are real-time, decentralized, and keep people's privacy safe. As cities depend more on smart devices and networked sensors, Edge Artificial Intelligence (Edge AI) and Federated Learning (FL) together offer a new way to undertake safe, scalable, and efficient urban data analytics. Edge AI lets you process data quickly and close to where it came from. This speeds up decision-making and cuts down on the requirement for cloud infrastructure. Federated Learning, on the other hand, lets several devices or organizations train a model simultaneously without having to share raw data. This maintains people's privacy and meets the standards for protecting data.

Introduction

One of the most major technological changes of the 21st century is the switch from conventional cities to smart cities. Smart cities use the latest technology, such as sensors, data analytics, cloud computing, and artificial intelligence (AI), to make city services work better, improve infrastructure, and make life better for people who live there. This transformation is happening because cities are growing quickly and their operations are becoming more complicated. These developments need clever, scalable systems that can make judgments on the spot. Smart cities are based on the idea of the Internet of Things (IoT). It helps devices talk to one other and collect, share, and analyze data in sectors including traffic control, environmental monitoring, public safety, energy management, and healthcare. But delivering data to centralized cloud servers for processing hasn't worked well for city services that need to change quickly and are sensitive to delay. People are often worried about the safety and privacy of their data when they process it on the cloud, which usually causes delays and utilizes too much bandwidth. Edge Artificial Intelligence (Edge AI) is a new type of computing that has been created to solve these challenges. Edge AI is putting AI algorithms right on edge devices so that choices can be made faster by processing data close to where it comes from. This makes the system work better, less reliant on central servers, and less prone to experience delays. Edge devices, on the other hand, often don't have a lot of processing power or storage capacity, which can make AI models that are already in use less complicated and less powerful. Federated Learning (FL) was created because more and more people want to keep their information safe. Federated Learning (FL) is a means to train machine learning models on many different devices or servers while keeping the raw data in one location. You can only send model updates between devices and the central server, not data. This design makes better use of bandwidth and decreases the risk to privacy. Combining Edge AI and Federated Learning is a good technique to deal with the reality that smart city surroundings have a lot of various sorts of data and are spread out. Edge AI lets you make inferences in real time and close to where you are, and FL makes sure that several nodes may learn together without putting critical data at risk. They work together to tackle some of the major flaws with traditional smart city systems, like how well they can develop, how long it takes to respond, and how safe the data is. This research investigates the synergistic integration of Edge AI with Federated Learning within the context of smart cities. It examines the technological underpinnings, proposes a stratified architectural framework, evaluates applications in specific domains, and enumerates the advantages and technical challenges associated with them. As cities become more connected and full of data, combining these two ideas is a long-term strategy to make cities that are smart, robust, and respect people's privacy. One of the most crucial technological changes of the 21st century is the shift from conventional cities to smart cities. Smart cities use the latest technology, such as sensors, data analytics, cloud computing, and artificial intelligence (AI), to improve the lives of their residents, their infrastructure, and the services they provide. This trend is happening because more people are moving to cities and cities are becoming more complicated to run. Cities require systems that are smart, can grow, and can make decisions in real time.

Smart cities are based on the idea of the Internet of Things (IoT). It lets devices talk to each other and share information in areas including traffic control, environmental monitoring, public safety, energy management, and healthcare. But the typical design that sends data to centralized cloud servers for processing doesn't work for urban services since they need to be able to change quickly and are sensitive to latency. Processing in the cloud usually takes longer, utilizes too much bandwidth, and makes customers worry about the safety and privacy of their data.

Edge Artificial Intelligence (Edge AI) is a novel technique to compute that can help with these issues. Edge AI puts AI algorithms directly on edge devices, which lets choices be made faster by analyzing data closer to where it originates from. This means that the system doesn't need as many centralized servers, has less latency, and is more responsive. But edge devices usually don't have a lot of processing power or storage capacity, which can make AI models that are already in use less powerful and less complicated.

Federated Learning (FL) was also created since it is becoming more and more important to secure people's privacy. Federated Learning (FL) is a means for several devices or servers to work together to train machine learning models while keeping the raw data in one place. The central server and devices only provide model updates, not data. This design not only protects privacy better, but it also makes the most of bandwidth.

Combining Edge AI and Federated Learning creates a useful way to deal with the different and spread-out nature of smart city environments. Edge AI lets you make conclusions in real time and in a certain area. FL lets numerous nodes learn together without putting important data at risk. They work together to solve some of the major difficulties with traditional smart city technologies, such as how well they perform, how long it takes for data to get to where it needs to go, and how safe it is.

This paper looks at how Edge AI and Federated Learning can work together to make smart cities better. It talks about the technological foundations, recommends a layered architectural model, looks at how it can be used in several sectors, and talks about the pros and cons of these uses. As cities become more connected and full of data, combining these two ideas is a long-term method to construct smart cities that are smart, respect people's privacy, and are robust.