1. McMahan, H.B., Moore, E., Ramage, D., y Arcas, B.A.: Federated learning of deep networks using model averaging. arXiv preprint https://arxiv.org/abs/1602.05629 (2016)
2. Konečnỳ, J., McMahan, H.B., Ramage, D., Richtárik, P.: Federated optimization: distributed machine learning for on-device intelligence. arXiv preprint https://arxiv.org/abs/1610.02527 (2016)
3. Reisizadeh, A., Mokhtari, A., Hassani, H., Jadbabaie, A., Pedarsani, R.: FedPAQ: a communication-efficient federated learning method with periodic averaging and quantization. In: International Conference on Artificial Intelligence and Statistics, pp. 2021–2031. PMLR (2020)
4. Mrad, I., Samara, L., Abdellatif, A.A., Al-Abbasi, A.O., Hamila, R., Erbad, A.: Federated learning for UAV swarms under class imbalance and power consumption constraints. In: 2021 IEEE Global Communications Conference (GLOBECOM), pp. 1–6 (2021)
5. Shurdi, O., Ruçi, L., Biberaj, A., Mesi, G.: 5G energy efficiency overview. Eur. Sci. J. 17, 315–327 (2021)