Value of Information and Timing-aware Scheduling for Federated Learning

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.

Towards 6G Non-Terrestrial Networks

Sixth-Generation (6G) technologies will revolutionize the wireless ecosystem by enabling the delivery of futuristic services through terrestrial and non-terrestrial transmissions. In this context, the Non-Terrestrial Network (NTN) is growing in importance owing to its capability to deliver services anywhere and anytime and also provide coverage in areas that are unreachable by any conventional Terrestrial Network (TN). The exploitation of the same radio technology could greatly facilitate the integration of NTNs and TNs into a unified wireless system. Since New Radio (NR) is the de facto standard to deliver manifold heterogeneous services in terrestrial wireless systems, 3GPP is investigating new solutions to extend NR to NTNs. In this paper, the constraints that NTN features place on NR procedures are investigated by going thoroughly into 3GPP specifications; strengths and weaknesses of the NR technology in enabling typical 6G services on NR-enabled NTNs are identified; finally, open issues and insights are provided as guidelines to steer future research towards 6G NTNs.