## 1 Introduction  
The Internet of Things (IoT) is revolutionizing various sectors by enabling massive data sharing and robust decision-making through connected devices. As a result, IoT scenarios are anticipated to generate large quantities of data in the near future [1]. To harness the potential of these data, researchers are investigating the feasibility of storing and processing them in cloud-based environments using advanced artificial intelligence techniques, such as deep learning and machine learning algorithms [2]. However, cloud-based data processing raises concerns about privacy and communication costs, which could potentially limit the benefits of such solutions [3]. Furthermore, IoT systems demand distributed intelligent services that can adapt to dynamic environments in real time, posing challenges due to the inherent complexity and heterogeneity of IoT devices. Researchers have proposed a multi-objective optimization technique using the Glowworm Swarm Optimization (GSO) algorithm and a hybrid optimization approach combining the artificial bee colony method, genetic operators, and density correlation degree to enhance the performance of blockchain-based Industrial Internet of Things (IIoT) systems [4, 5]. Federated learning (FL), a distributed machine learning framework that ensures privacy, reduces data transfer, and solves device heterogeneity while allowing real-time adaptation to various contexts, has thus emerged as a solution to these problems [6]. FL is a distributed machine learning technique that allows for the training of algorithms across several edge devices, each of which has local datasets that are not shared or exchanged [7]. By locally training models on IoT devices and aggregating the trained parameters on a central server, FL addresses data privacy concerns and adapts to the heterogeneity of IoT environments. FL has been successfully applied in various applications, such as mobile keyboard prediction [8], autonomous industry [9], remote health monitoring [10], and medical image processing [11], demonstrating its significant potential. By leveraging local computing capabilities and ensuring data security through local model training, FL not only improves training efficiency but also enhances privacy protection and model accuracy. Recently, FL methods have been used in IoT-based systems to enable privacy-enhanced IoT systems by allowing multiple devices to coordinate with a central server for data training without sharing actual datasets [12]. In IoT networks, devices act as workers communicating with an aggregator, performing neural network training to enhance training quality and minimize user privacy leakage. The robustness and potential of FL frameworks make them fundamental building blocks for sophisticated IoT applications. Figure 1 shows the general design of FL in an IoT context, where IoT devices train their local models and send weights to the FL server. The FL server gathers all local model weights from IoT devices and updates its global model to acquire global model weights. Although the potential of FL in IoT scenarios is huge, there are shortcomings of FL that need to be solved to implement FL in heterogeneous industrial IoT networks. Figure 2 illustrates a scenario of heterogeneous FL in an IoT environment. In heterogeneous scenarios, clients do not share the same computational and energy resources. The dataset is dispersed uniquely, each with distinct attributes and properties. The aggregation from these IoT devices affects the performance of the global model due to inadequate training of clients. Therefore, performance optimization in such scenarios requires approaches that can handle the variations in computational and data distributions. Figure 1: A generic architecture of the FL framework for IoT scenarios. Despite these variations in scenarios, a majority of existing methods continue to employ identical training configurations (epochs) and model aggregation methods, irrespective of the scenario at hand.