Android malware has presented serious obstacles to mobile security, which have conventionally been met by centralized machine learning models that invade privacy of users by necessitating the collective compilation of sensitive information. The paper will discuss a Federated Learning (FL) architecture that preserves privacy and uses Deep Neural Networks (DNN) to detect Android malware. With the use of the CIC MalDroid dataset we simulate several clients, each having a mixture of benign applications and different types of malware. Local models are developed on client devices and then combined with the Federated Averaging (FedAvg) algorithm, which realize a promising detection accuracy of 95.86 %. In order to increase the detection capability, local client weights are applied to the global model resulting in local performance improvements and false positives are minimized. The experimental findings prove that the proposed framework is not only highly accurate but also ensures strong generalization among the clients, therefore, the issue of privacy is resolved by storing sensitive data on the local devices.