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About this paper
Saadat Izadi, Shakib Komasi, Ali Salimi, Alireza Rezaei, Mahmood Ahmadi
The authors propose an IoT intrusion-detection system combining Mist-layer feature abstraction with Fog-level FedProx to address input and client-data heterogeneity. A lightweight encoder and MLP classifier train without centralizing raw device data. On TON-IoT, the method reports 98.52% accuracy versus 97.06% for Wang et al.; this cross-study comparison does not isolate components, and accuracy falls and varies more at 90 to 100 clients.
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