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Smart city article details
| Title | Federated Learning Shaping The Future Of Smart City Infrastructure |
|---|---|
| ID_Doc | 26367 |
| Authors | Verma R.K.; Kishor K.; Galletta A. |
| Year | 2024 |
| Published | Federated Learning for Smart Communication using IoT Application |
| DOI | http://dx.doi.org/10.1201/9781003489368-10 |
| Abstract | In an age of urbanization and technology, smart cities may address urban problems. Smart city infrastructure incorporates advanced federated learning. Federated learning may transform smart city infrastructure, claims a report. Edge devices may train models jointly using federated learning to protect data. Federated learning keeps data local, boosting privacy and security over centralized systems. Federated learning helps smart city stakeholders analyze massive heterogeneous data without sacrificing privacy. Smart cities use local data live with federated learning. Smart cities use federated learning algorithms and sensor and IoT device model training to make fast decisions and respond to dynamic urban environments. Network congestion, data bottlenecks, and server transmission delays are reduced by this distributed method. Federated learning customizes smart city services and experiences. By training machine learning models on smartphones and wearables, personalized recommendations and predictive analytics may be offered without compromising sensitive data. Federated learning systems may improve mobility or send energy-saving advice home. Public safety and security are smart city-federated learning uses. Federated learning models may identify abnormalities, anticipate crime hotspots, and speed emergency response using security cameras, social media, and IoT data. Federated learning-based traffic management systems may modify traffic signals based on real-time traffic flow data, lowering fuel consumption and emissions and enhancing mobility. Federated learning presents smart city implementation challenges despite its promise. Issues include data heterogeneity, communication overhead, model synchronization, and algorithmic bias. To balance data usefulness, privacy, and computing efficiency, researchers, governments, and industry stakeholders must work. Federated learning’s fast, decentralized, and privacy-preserving data analysis may affect smart city infrastructure. Federated learning provides smart city stakeholders with actionable information, tailored services, public safety, and sustainability via dispersed edge device knowledge. Federated learning in smart cities may be possible by overcoming technological, legislative, and social barriers to equitable urban growth. © 2025 selection and editorial matter, Kaushal Kishor, Parma Nand, Vishal Jain, Neetesh Saxena, Gaurav Agarwal, Rani Astya; individual chapters, the contributors. |
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