  Over past years, the manually methods to create detection rules were no
longer practical in the anti-malware product since the number of malware
threats has been growing. Thus, the turn to the machine learning approaches is
a promising way to make the malware recognition more efficient. The traditional
centralized machine learning requires a large amount of data to train a model
with excellent performance. To boost the malware detection, the training data
might be on various kind of data sources such as data on host, network and
cloud-based anti-malware components, or even, data from different enterprises.
To avoid the expenses of data collection as well as the leakage of private
data, we present a federated learning system to identify malwares through the
behavioural graphs, i.e., system call dependency graphs. It is based on a deep
learning model including a graph autoencoder and a multi-classifier module.
This model is trained by a secure learning protocol among clients to preserve
the private data against the inference attacks. Using the model to identify
malwares, we achieve the accuracy of 85\% for the homogeneous graph data and
93\% for the inhomogeneous graph data.
