Federated Learning for Malware Image Classification under Data Heterogeneity
First, we present an empirical study of federated MIC under varying degrees of data heterogeneity across three popular malware image datasets (MalImg, Virus-MNIST, and MalNet).
ProofPaper ↗
Key points
- Malware image classification (MIC), in which binary files are converted into visual representations and deep neural networks are trained to identify malware families, has recently emerged as an effective approach for malware detection.
- Federated learning (FL) enables malware image classifiers to be trained collaboratively by multiple clients without sharing raw data.
- Second, we introduce a set of round-level statistics that characterize the data contributed by participating clients in each FL round, and demonstrate that class entropy (diversity of class labels present in a given round) exhibits strong positive correlation with global model accuracy.
- Third, leveraging this insight, we propose an entropy-aware client selection strategy that filters candidate client subsets based on class entropy, and show that our strategy outperforms standard random selection.
Sources (1)
- [1]Federated Learning for Malware Image Classification under Data HeterogeneityarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 01:34 PM
First, we present an empirical study of federated MIC under varying degrees of data heterogeneity across three popular malware image datasets (MalImg, Virus-MNIST, and MalNet).
Malware image classification (MIC), in which binary files are converted into visual representations and deep neural networks are trained to identify malware families, has recently emerged as an effective approach for malware detection.
Extractive summary: sentences quoted from the sources.