Otimização de Desempenho de Análise Federada para Redes de Próxima Geração (B5G/6G) - Sebastião, Xavier Paulino (2025)
The rapid growth of interconnected devices across various sectors in the world has led to the generation of large volumes of data. Depending on its nature and specific needs, a significant part of this data is handled and analyzed using data science techniques to support decision-making. Alongside these advancements, as institutions rely more on data-driven systems, they also face increasing threats and security challenges that compromise the privacy of their clients or collaborators, consequently damaging their reputation. Federated Analytics (FA) is an innovative approach for preserving data security and privacy by implementing collaborative analysis of data from distributed devices without sharing raw data. However, in cases where FA operates over wireless transmission, challenges such as interference, signal degradation, and network congestion may arise. These factors can make the wireless transmission unreliable, introducing delays and causing corruption in responses and updates received at the central server, thereby compromising the quality of the final aggregated FA results. This work proposes an integrated framework for simulating FA in real 5G network conditions. The framework applies two algorithms: channel-aware power allocation algorithm to efficiently allocate transmission power for user equipments (UEs) based on distance and channel conditions, and synchronous FA5GLENA integrated algorithm to integrate the FA with NS-3 5G-LENA and aggregate results for optimized performance within 5G network conditions. To evaluate the impact of the network on FA, three scenarios were compared: (1) uniform maximum power allocation for all UEs, (2) random power allocation, and (3) channel-aware power allocation algorithm. The simulation results show that the channel-aware algorithm outperforms the uniform and random power allocation scenarios on both the network and FA operation. On FA, the algorithm achieved statistically higher accuracy (93.17 %), precision (93.31 %), and recall (93.09 %) compared to uniform allocation (accuracy: 55.96 %, precision: 56.02 %, recall: 55.90 %) and random allocation (accuracy: 42 %, precision: 42.02 %, recall: 41.96 %), highlighting the superiority of the algorithm in enhancing FA performance.