1. B. McMahan, E. Moore, D. Ramage, S. Hampson, B.A. y Arcas, Communication-Efficient Learning of Deep Networks from Decentralized Data, in: A. Singh, J. Zhu (Eds.), Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, in: Proceedings of Machine Learning Research, vol. 54, Fort Lauderdale, FL, USA, 2017, pp. 1273–1282.
2. Advances and open problems in federated learning;Kairouz,2021
3. On the tradeoff between energy, precision, and accuracy in federated quantized neural networks;Kim,2023
4. An energy and carbon footprint analysis of distributed and federated learning;Savazzi;IEEE Trans. Green Commun. Netw.,2023
5. 2D federated learning for personalized human activity recognition in cyber-physical-social systems;Zhou;IEEE Trans. Netw. Sci. Eng.,2022