I. Introduction
The continually increasing data generated at edge devices stems from billions of interconnected Internet-of-Things (IoT) devices as every active IoT client gathers its observed information and forwards it to the edge. Traditional Machine Learning (ML) methods typically aggregate the gathered data on a central data center or a single machine, and such a centralized learning scheme is common among AI-driven companies such as Google and Microsoft [1]. To enhance model performance using this centralized approach, users might have to sacrifice their privacy by transmitting personal data to these centers. This training approach can be intrusive to privacy, especially when individuals have to share personal or sensitive information to improve the training model's performance. However, the emergence of Federated Learning (FL) happened to overcome these challenges. FL is an innovative approach to centralized ML where a model is trained across multiple IoT devices or servers while keeping the data localized [2]. Instead of sending the data to a central server for training, FL sends the model to the IoT devices. Each device computes an update to the model based on its local data, and then sends only this update back to the central server, where it is aggregated with updates from other devices to improve the global model.