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.

EDGEmergency is a platform designed for disaster management that can dynamically leverage the edge infrastructure potentially already present within the emergency perimeter. Edge devices, from IoT to smartphones, possess an increasingly significant computational capacity that can be exploited, by changing their behavior in real-time and creating a pervasive local environment, capable of adapting perfectly to the specific context of reference. EDGEmergency, in fact, allows the creation of a unified computation environment leveraging the Cloud-Edge-Client Continuum concept, through which a computation cluster with zero configurations is created on-the-fly. The platform thus allows the deployment of distributed microservices on existing edge devices, installed by default for other purposes, through a modular and incremental logic that has the role of adapting best to the needs of the individual emergency, through advanced tools for analysis and monitoring, using artificial intelligence.

Nowadays, thanks to the ever-increasing hardware capacity of Edge computing, the achievement of Ubiquitous Computing is no longer a utopia, even though it presents still several challenges. In this paper, we introduce the concept of the Cloud-Edge-Client Continuum, by extending the well-known Cloud-Edge Continuum paradigm with the addition of Clients as deployment nodes. Specifically, we propose both a system architecture and a piece of middleware that allows a web browser to be used seamlessly as a deployment Client node, introducing the concept of a Virtual Point of Deployment (VPod). Our solution allows to: a) leverage the computational capacity of a huge number of ready-to-use devices that do not require the installation of any dependencies; b) optimize the use of resources with clear benefits for end users, who can take advantage of their computing capacity to process sensitive data; c) reduce infrastructure costs. In addition, our proposal opens toward a multitude of scenarios, as the logical division that exists in the common client-server architecture is overcome, enabling the creation of a Cloud-Edge-Client Continuum environment.