1. 1) B. McMahan, E. Moore, D. Ramage, S. Hampson, and B.A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” Proc. AISTATS, Fort Lauderdale, Florida, USA, April 2017.
2. 2) T. Li, A.K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Process. Mag., vol.37, no.3, pp.50-60, 2020.
3. 3) L.U. Khan, W. Saad, Z. Han, E. Hossain, and C.S. Hong, “Federated learning for internet of things: Recent advances, taxonomy, and open challenges,” IEEE Commun. Surveys Tuts., vol.23, no.3, pp.1759-1799, 2021.
4. 4) C.M. Bishop, Pattern Recognition and Machine Learning, Springer-Verlag, Berlin, Heidelberg, 2006.
5. 5) 斎藤康毅,ゼロから作るDeep Learning—Pythonで学ぶディープラーニングの理論と実装—, O'Reilly Japan, 2016.