## 1 Introduction
However, the limited resources of IoT devices often render the implementation of traditional machine learning and deep learning approaches impractical. These methods typically excel in complex environments where significant computational power, memory, and large volumes of data are available [24]. Additionally, machine learning and deep learning techniques are generally centralized, requiring both data and models to be stored at a central location [20; 24]. Moreover, transferring large volumes of data to a centralized location can be communication-intensive and may compromise data privacy while also negatively impacting the lifespan of IoT devices and the overall network. As a result, traditional machine learning and deep learning approaches may not be well suited to IoT applications [25]. Federated learning (FL) and data fusion represent two distinct methodologies for managing data in IoT networks, each with unique strengths and limitations. FL prioritizes privacy and decentralization, enabling IoT nodes to locally train models and exchange only model updates. This approach reduces communication overhead and preserves sensitive data, making it well suited for energy-constrained and privacy-critical applications. Conversely, data fusion involves aggregating raw sensory data from multiple nodes at a central location, which can achieve high accuracy but at the expense of higher energy consumption, increased communication costs, and limited scalability in large-scale deployments [26; 27; 28]. A key distinction lies in data handling; FL keeps raw data local, whereas data fusion relies on transferring raw data to a central server for analysis. This centralized approach introduces significant communication overhead, particularly in resource-constrained IoT networks. FL overcomes this challenge by transmitting lightweight model updates instead of bulk raw data, making it a more efficient option for modern IoT applications requiring distributed intelligence. While FL aligns with the needs of dynamic and heterogeneous IoT environments, data fusion remains advantageous for traditional setups where centralized processing and immediate access to raw data are essential. Recently, FL has emerged as a promising distributed data processing solution [20; 22; 29; 30; 31]. Since its introduction by Google, FL has garnered significant attention for its ability to operate in a decentralized manner, primarily focusing on preserving data privacy [32]. In FL, IoT devices collaborate to create a powerful model that benefits all participants by sharing insights without compromising individual privacy. Each device can learn from other devices, thereby expanding its knowledge beyond its own limited local dataset [22]. Results have shown that the performance of FL is comparable to traditional centralized machine learning methods [25]. FL emphasizes several key aspects, including scalability, communication efficiency, device heterogeneity, statistical heterogeneity, and data privacy [20; 33]. However, many machine learning architectures are resource-intensive, requiring substantial memory, processing, and communication power [22]. Additionally, traditional CPUs and GPUs may not be suitable for IoT devices, which are often untethered and battery-powered [22]. Consequently, training deep learning models on IoT devices is typically time-consuming and inefficient [22]. FL algorithms are designed to enable multiple nodes to collaboratively train a global model in a massively distributed manner [22; 34]. Generally, FL decouples model training from the need to directly access raw local data, allowing nodes to maintain the privacy of their data. Instead of exchanging local data, nodes communicate model updates to the server after performing local training [20; 22; 25; 35]. As a result, the global model is developed and refined at the server.