TERAIYA, M.; SHUKLA, M. Privacy-Utility-Efficiency Trade-Offs in Personalized Federated Learning for Edge Computing: Clipping-Pressure Diagnostics and Pareto Operating Points for Privacy-Preserving Edge Learning. Engineering, Technology & Applied Science Research, Greece, v. 16, n. 2, p. 33434–33442, 2026. DOI: 10.48084/etasr.17222. Disponível em: https://mail.etasr.com/index.php/ETASR/article/view/17222. Acesso em: 17 apr. 2026.