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Title	Federated Learning For Anomaly Detection In Smart Cities
ID_Doc	26336
Authors	Mukherjee P.; Khwaja A.S.; Anpalagan A.; Baljon M.
Year	2025
Published	10th International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2025
DOI	http://dx.doi.org/10.1109/WiSPNET64060.2025.11004967
Abstract	Federated Learning (FL) enables privacy-preserving model training on distributed devices without sharing data between them. This paper presents an improved FL framework for anomaly detection in Internet of things (IoT)-based smart cities, specifically addressing non-independent and identically distributed (non-IID) data challenges. Unlike traditional methods, this approach uses the federated proximal (FedProx) algorithm and integrates transformers and autoencoders to enhance pattern recognition and anomaly detection in heterogeneous non-IID IoT data. The study compares federated averaging (FedAvg) and Fed-Prox algorithms, demonstrating that the latter, combined with the enhancements, outperforms the former, achieving an accuracy of 86.43%. These results highlight FedProx's superior performance in handling non-IID data in real-world IoT environments. © 2025 IEEE.
Author Keywords	anomaly detection; autoencoders; Federated learning; FedProx; IoT; non-IID data; transformers

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