Malware is overgrowing, causing severe loss to different institutions. The existing techniques, like static and dynamic analysis, fail to mitigate newly generated malware. Also, the signature, behavior, and anomaly-based defense mechanisms are susceptible to obfuscation and polymorphism attacks. With machine learning in practice, several authors proposed different classification and visualization techniques for malware detection. Images have proved worth analyzing the behavior of malware. Deep neural networks extract much information from it without having expert domain knowledge. On the other hand, the scarcity of diverse malware data available with clients, and their privacy concerns about sharing data with a centralized curator makes it challenging to build a more reliable model. This paper proposes a lightweight Convolution Neural Network (CNN) based model extracting relevant features using call graph, n-gram, and image transformations. Further, Auxiliary Classifier Generative Adversarial Network (AC-GAN) is used for generating unseen data for training purposes. The model is extended for federated setup to build an effective malware detection system. We have used the Microsoft malware dataset for training and evaluation. The result shows that the federated approach achieves the accuracy closer to centralized training while preserving data privacy at an individual organization.