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federated learning IoT Dataset Records | IoTDataset.com

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Kaggle
Cybersecurity Feb 05, 2026

Gotham Dataset 2025: Large-Scale Federated IoT IDS Benchmark

The Gotham Dataset is a large-scale, reproducible benchmark for evaluating decentralized Intrusion Detection Systems (IDS) and Federated Learning in virtualized smart cities. It captures interface-level network traffic from 78 heterogeneous IoT devices, including complex attack vectors like Mirai botnets, Merlin C2 traffic, and CoAP amplification, preserving the non-IID nature of edge data for realistic AI security training.

Scientific Reports (Nature Publishing Group)

Dataset-Centric Evaluation of Federated Intrusion Detection Models in IoT Networks

A federated learning evaluation across several contemporary IoT and IIoT intrusion detection datasets, benchmarking algorithms such as FedAvg, FedProx, and FedNova with LSTM and Transformer models in in-domain, cross-dataset, and multi-dataset federation scenarios.