Android malware attacks are increasing daily at a tremendous volume, making
Android users more vulnerable to cyber-attacks. Researchers have developed many
machine learning (ML)/ deep learning (DL) techniques to detect and mitigate
android malware attacks. However, due to technological advancement, there is a
rise in android mobile devices. Furthermore, the devices are geographically
dispersed, resulting in distributed data. In such scenario, traditional ML/DL
techniques are infeasible since all of these approaches require the data to be
kept in a central system; this may provide a problem for user privacy because
of the massive proliferation of Android mobile devices; putting the data in a
central system creates an overhead. Also, the traditional ML/DL-based android
malware classification techniques are not scalable. Researchers have proposed
federated learning (FL) based android malware classification system to solve
the privacy preservation and scalability with high classification performance.
In traditional FL, Federated Averaging (FedAvg) is utilized to construct the
global model at each round by merging all of the local models obtained from all
of the customers that participated in the FL. However, the conventional FedAvg
has a disadvantage: if one poor-performing local model is included in global
model development for each round, it may result in an under-performing global
model. Because FedAvg favors all local models equally when averaging. To
address this issue, our main objective in this work is to design a dynamic
weighted federated averaging (DW-FedAvg) strategy in which the weights for each
local model are automatically updated based on their performance at the client.
The DW-FedAvg is evaluated using four popular benchmark datasets, Melgenome,
Drebin, Kronodroid and Tuandromd used in android malware classification

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