Antonio Iera

Muhammad Azeem Khan, Howard H. Yang, Zihan Chen et al.

Data possesses significant value as it fuels advancements in AI. However, protecting the privacy of the data generated by end-user devices has become crucial. Federated Learning (FL) offers a solution by preserving data privacy during training. FL brings the model directly to User Equipments (UEs) for local training by an access point (AP). The AP periodically aggregates trained parameters from UEs, enhancing the model and sending it back to them. However, due to communication constraints, only a subset of UEs can update parameters during each global aggregation. Consequently, developing innovative scheduling algorithms is vital to enable complete FL implementation and enhance FL convergence. In this paper, we present a scheduling policy combining Age of Update (AoU) concepts and data Shapley metrics. This policy considers the freshness and value of received parameter updates from individual data sources and real-time channel conditions to enhance FL's operational efficiency. The proposed algorithm is simple, and its effectiveness is demonstrated through simulations.

Mattia Giovanni Spina, Edoardo Scalzo, Floriano De Rango et al.

The rise of programmable data plane (PDP) and in-network computing (INC) paradigms paves the way for the development of network devices (switches, network interface cards, etc.) capable of performing advanced processing tasks. This allows running various types of algorithms, including machine learning, within the network itself to support user and network services. In particular, this paper delves into the deployment of in-network learning models with the aim of implementing fully distributed intrusion detection systems (IDS) or intrusion prevention systems (IPS). Specifically, a model is proposed for the optimal distribution of the IDS/IPS workload among data plane devices with the aim of ensuring complete network security without excessively burdening the normal operations of the devices. Furthermore, a meta-heuristic approach is proposed to reduce the long computation time required by the exact solution provided by the mathematical model and its performance is evaluated. The analysis conducted and the results obtained demonstrate the enormous potential of the proposed new approach for the creation of intelligent data planes that act effectively and autonomously as the first line of defense against cyber attacks, with minimal additional workload on the network devices involved.

Leonardo Militano, Antonino Orsino, Giuseppe Araniti et al.

In the future standardization of the 5G networks, in Long Term Evolution (LTE) Release 13 and beyond, Device-to-Device communications (D2D) is recognized as one of the key technologies that will support the 5G architecture. In fact, D2D can be exploited for different proximity-based services (ProSe) where the users discover their neighbors and benefit form different services like social applications, advertisement, public safety, and warning messages. In such a scenario, the aim is to manage in a proper way the radio spectrum and the energy consumption to provide high Quality of Experience (QoE) and better Quality of Services (QoS). To reach this goal, in this paper we propose a novel D2D-based uploading scheme in order to decrease the amount of radio resources needed to upload to the eNodeB a certain multimedia content. As a further improvement, the proposed scheme enhances the energy consumption of the users in the network, without affects the content uploading time. The obtained results show that our scheme achieves a gain of about 35\% in term of mean radio resources used with respect to the standard LTE cellular approach. In addition, it is also 40 times more efficient in terms of energy consumption needed to upload the multimedia content.

Giuseppe Araniti, Massimo Condoluci, Antonino Orsino et al.

This paper addresses a multi-criteria decision method properly designed to effectively evaluate the most performing strategy for multicast content delivery in Long Term Evolution (LTE) and beyond systems. We compared the legacy conservative-based approach with other promising strategies in literature, i.e., opportunistic multicasting and subgroup-based policies tailored to exploit different cost functions, such as maximum throughput, proportional fairness and the multicast dissatisfaction index (MDI). We provide a comparison among above schemes in terms of aggregate data rate (ADR), fairness and spectral efficiency. We further design a multi-criteria decision making method, namely TOPSIS, to evaluate through a single mark the overall performance of considered strategies. The obtained results show that the MDI subgrouping strategy represents the most suitable approach for multicast content delivery as it provides the most promising trade-off between the fairness and the throughput achieved by the multicast members.