## 1 Introduction
FL approaches encounter several challenges, including the high dimensionality of model updates, persistent high communication costs, the heterogeneity of IoT devices, varying computational capabilities, varied power consumption, data quality, and data security and privacy [20; 36]. Large-scale deployments of IoT devices that collect diverse sensory data can lead to significant communication costs and bottlenecks when all devices tend to transmit data to the cloud or base-station [20]. Data interoperability is another critical challenge in implementing FL within IoT networks, as each device may collect data in different structures, sizes, formats, and types [24]. Consequently, IoT devices typically refrain from sending unprocessed raw data to the base-station. Instead, sensory data are collaboratively processed among the IoT devices before being forwarded to the base-station or cloud. FL has been proposed to improve many IoT applications. In healthcare, FL can be utilized to accelerate patient diagnosis and treatment without compromising the privacy of patients. In these scenarios, patient, hospitals, and healthcare policy makers do not need to exchange their healthcare records with each other; instead, FL can train IoT devices and smartphones locally and communicate results to a server for a global consensus [37]. Seamless services such as the delivery of traffic and energy can benefit from FL to improve smart city applications [38]. FL offers smart city applications a medium for decentralized data processing that can preserve the privacy of users and IoT devices' data. A smart traffic system was proposed in [39], where smart vehicles learn an FL model based on a traffic sign dataset; the application allows selected vehicles to collaborate in a decentralized manner to recognize unlabeled traffic data while not sharing their data with third parties. Moreover, in [40] the locations of chargers are shared amongst electric vehicles without revealing private information. Videos from smartphones, bus cameras, CCTV cameras, and videos from other IoT devices are utilized in an FL video manage ment application, where each IoT device performs local video analytics and shares results only. Although FL and IoT have been studied extensively in recent years, their communication overhead and energy consumption implications remain unexplored. Many FL algorithms are communication-intensive and as a result are not suitable for resource-constrained, battery-operated untethered IoT devices. These limitations motivate us to suggest energy-efficient and in-network processing approaches to improve the performance of FL applications. FL has been proposed as a transformative approach through which to enhance various IoT applications. In particular, FL is highly compatible with healthcare systems, where data privacy is paramount due to the sensitive nature of healthcare records. Patient data are often distributed across hospitals and healthcare facilities, making FL an ideal solution. By leveraging FL, patient diagnosis and treatment processes can be accelerated without compromising privacy. In these scenarios, patients, hospitals, and policy makers do not need to exchange raw healthcare records. Instead, FL enables IoT devices and smartphones to train models locally, sharing only aggregated results with a central server to achieve a global consensus [37]. Beyond healthcare, FL also benefits smart city applications, such as traffic management and energy delivery [38]. Smart city sensing is a critical component of any smart city system, and FL provides a decentralized framework for data processing while safeguarding the privacy of users and IoT devices. For example, [39] proposed a smart traffic system where vehicles collaboratively train an FL model using a traffic sign dataset. This approach allows selected vehicles to recognize unlabeled traffic data in a decentralized manner without sharing sensitive information with third parties.