Federated Learning (FL) aims to train artificial intelligence models without the need to share private raw data, thereby preserving privacy and security. Typically, it is assumed that all participating FL clients will act honestly to develop an accurate model. However, some clients may behave deceptively, manipulating their data to bias the model’s predictions and also degrade its generalization ability. This paper addresses the issue of fairness in FL from the perspective of client truthfulness. We introduce Federated Objective (FedObj), a novel aggregation method designed to minimize the impact of malicious clients and thereby improve the overall model’s robustness to such behavior. Our results show that FedObj achieves state-of-the-art performance in standard scenarios and outperforms conventional strategies when deceptive clients are involved. FedObj is a valuable approach for the collaborative development of trustworthy and fair AI systems, as it is significantly resilient to the misleading practices of malicious FL clients.

Internet of Things (IoT) became more and more popular because of the raise of ubiquitous internet connected devices. In this regard, IoT nodes are often organized in wireless sensor networks to facilitate communication and perform a coral computation. Such a network is often employed in urban or rural areas, i.e., for traffic, fires, and floods monitoring. Nodes are, therefore, deployed in remote areas, preventing the possibility to frequently access them, i.e., for firmware update. In this context, over-the-air (OTA) firmware update is used to remotely change the behavior of one or more nodes. In this paper, we firstly build a wireless mesh network with microcontrollers (i.e., ESP32) and, therefore, propose a secure filtered O TA firmware update involving firmware freshness (i.e., quarantine when firmware is not up-to-date), key pairing, and digital signature for data integrity and non-repudiation. The system is evaluated in terms of deactivation time $(s)$, energy consumption $(kWh)$, and greenhouse gases $(\mathrm{kgCO}_{2}\mathrm{e})$, highlighting good results in terms of scalability for grouped updates.