How does the performance of federated learning models (like FEDetect) compare to centralized deep neural netwo

Abstract

Abstract: This work investigates the possibilities enabled by federated learning concerning IoT malware detection and studies security issues inherent to this new learning paradigm. In this context, a framework that uses federated learning to detect malware affecting IoT devices is presented. N-BaIoT, a dataset modeling network traffic of several real IoT devices while affected by malware, has been used to evaluate the proposed framework. Both supervised and unsupervised federated models (multi-layer perceptron and autoencoder) able to detect malware affecting seen and unseen IoT devices of N-BaIoT have

Research Question

How does the performance of federated learning models (like FEDetect) compare to centralized deep neural networks in terms of detection latency and throughput when evaluated on the AndroZoo benchmark with varying levels of obfuscation?

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Correction Record

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Provenance