Fedhgcdroid: an adaptive multi-dimensional federated learning for privacy-preserving android malware classification

HIGHLIGHTS

• who: Changnan Jiang et al. from the Key Laboratory of Beijing Network Technology, Beihang University, Beijing, China have published the article: FedHGCDroid: An Adaptive Multi-Dimensional Federated Learning for Privacy-Preserving Android Malware Classification, in the Journal: Entropy 2022, 24, 919. of /2022/
• what: Problem the authors propose a new scheme based on Federated learning named FedHGCDroid which classifies on clients in a privacy-protected manner. To adapt to the non-IID distribution of on clients the authors propose a contribution degree-based adaptive classifier training mechanism FedAdapt to improve the adaptability of the classifier . . .
  

   

  

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