A Comparative Study of Federated Learning Frameworks for IoT-Driven Smart Cities
DOI:
https://doi.org/10.5433/1679-0375.2026.v47.53937Keywords:
smart city, internet of things, machine learning, federated learning, privacyAbstract
The integration of Machine Learning (ML) with smart cities offers promising solutions for analyzing IoT (Internet of Things) data. However, conventional ML approaches often struggle to balance accuracy and computational costs, particularly in heterogeneous and resource-constrained IoT environments. Federated Learning (FL) has emerged as an alternative, enabling privacy-preserving collaborative learning across distributed devices while reducing centralized data collection. Over the past decade, both industry and academia have contributed to a growing ecosystem of FL frameworks with distinct functionalities and design choices. Despite this progress, research on FL in smart cities remains limited, making it difficult to assess framework suitability for real-world use cases. This paper presents a comparative analysis of FL frameworks in engineering-oriented smart city scenarios. We evaluate representative frameworks, focusing on Flower and NVFlare, using datasets derived from IoT infrastructures. The results highlight the trade-offs of FL compared to conventional ML, offering insights for researchers, engineers, and decision-makers.
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