## 2 Related Works  
Nowadays, privacy protection provided by FL is more robust and efficient than was the case with the traditional ML model, while FL still ensures a similar prediction accuracy. As stated in a survey study [11], recent security defense instances [12; 13; 14; 15; 16; 17; 18] are also being developed based on the FL framework, including federated learning-based intrusion detection systems and anomaly detection. In the Internet of Things (IoT) Industry 4.0, the authors in [14] proposed a collaborative intrusion detection system (IDS) in which there are filters performing a deep neural network (DNN) and a central server collecting the filters' DNN parameters to generate a global model. Recently, researchers have started to employ FL ( federated learning) technology to achieve privacy protection and high accuracy in Android malware detection and classification. In [19], the authors use federated learning, combining data from multiple users, to improve malware detection and ensure privacy preservation. The authors in [20] propose another framework based on a combination of semi-supervised machine learning and federated learning. Its focus is on maintaining user privacy and it employs a semi-supervised machine learning technique that reportedly improves classification accuracy. The authors in [21] propose LiM, a malware classification framework that leverages the power of FL to detect and classify malicious apps in a privacy-respecting manner. The methodology in [22] is distinctive because it integrates federated learning (FL) with a novel classification model to protect user privacy while effectively identifying malware. These works employ an FL framework to enable distributed android clients to collaboratively train a comprehensive Android malware detection or classification in a privacy-preserving manner. However, if FL faces cybersecurity attacks, all applications using this framework might also be exposed to several security risks, such as private user data leakage and reduced accuracy. This study aims to propose a new robust FL framework resistant to cybersecurity attacks. Our proposed FL framework can ensure that FL can resist multiple attacks and maintain high accuracy under various network scenarios. The aim of cybersecurity attacks in the FL framework is to make the global model unstable and incapable of making accurate predictions. There are typically three main targets that the attacker might focus on. The first target is the local data set. The second is the local model maintained by the local edge client. The final one is the central server. The biased global model, once distributed to all edge clients, leads to incorrect label predictions for the inputs locally. Specifically, federated learning with an easy FL algorithm, e.g., FegAvg, is highly vulnerable to poisoning attacks, even if only one fake client is involved in the FL framework. The famous case of the Byzantine problem [23] can cause fatal damage to the quality of the global model in an FL framework applying a FedAvg scheme. In order to confront such a poisoning attack, some new FL algorithms are proposed, like Krum [24] and Trimmed-mean [25]. Filtering possibly malicious model updates is an efficient method to prevent the global model from being influenced by poisoning attacks. Recently, a novel cybersecurity defense algorithm [9] for FL frameworks, called FLcert, was proposed. The proposed key idea divided the edge clients participating in the FL framework into several groups. According to their grouping methodology, there are two variants of grouping schemes, FLcert-P and FL Cert-D. In each group, each edge client trains its local model as a usual FL framework. Instead of sending local model parameters to the central server, the edge clients send local model parameters to their corresponding group leaders. These group leaders execute a similar job to that of the central server in a traditional FL framework, generating the group's global model with an FL algorithm.