A Federated Learning Framework for Smart Grids: Securing Power Traces in Collaborative Learning

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” in Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997

1997

Earlier work this paper cites.

P. Paillier, “Public-key cryptosystems based on composite degree residuosity classe,” Proc. of International Conference on the Theory and Applications of Cryptographic Techniques, pp. 223-238, 1999

1999

Earlier work this paper cites.

J. Rifkin, “The third industrial revolution: how lateral power is transforming energy, the economy, and the world,” in Macmillan, 2011

2011

Earlier work this paper cites.

D. Peteiro-Barral and B. Guijarro-Berdinas, “A survey of methods for distributed machine learning,” in Progress in Artificial Intelligence, vol. 2, no. 1, pp. 1-11, 2013

2013

Earlier work this paper cites.

J. Konecny, H. B. McMahan, F. X. Yu, A. T. Suresh and D. Bacon, “Federated Learning: Strategies for Improving Communication Efficiency,” arXiv preprint arXiv:1610.05492, 2016

Original

2016

Earlier work this paper cites.

T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” KDD ’16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, pp. 785–794, 2016

2016

Earlier work this paper cites.

H. B. McMahan, E. Moore, D. Ramage et al, “Communication-efficient learning of deep networks from decetralized data,” arXiv preprint arXiv:1602.05629, 2016

Original

2016

Earlier work this paper cites.

P. Voigt and V. dem Bussche, “The EU General Data Protection Regulation (GDPR),” 1st Ed., Cham: Springer International Publishing, vol. 10, pp. 3152676, 2017

2017

Earlier work this paper cites.

B. McMahan and D. Ramage, “Federated Learning: Collaborative Machine Learning without Centralized Training Data”, Google AI Blog, 2017 [Online]. Available: https://ai.googleblog.com/2017/04/federated-learning-collaborative.html

2017

Earlier work this paper cites.

Y. Chen, Y. Wang, D. Kirschen and B. Zhang, “Model-Free Renewable Scenario Generation Using Generative Adversarial Networks,” in IEEE Transactions on Power Systems, vol. 33, no. 3, pp. 3265-3275, May 2018

2018

Cited alongside, same era.

T. S. Brisimi, R. Chen, T. Mela et al, “Federated learning of predictive models from federated Electronic Health Records,” in International Journal of Medical Informatics, vol. 112, pp. 59-67, 2018

2018

Cited alongside, same era.

Y. Chen, Y. Wang, D. Kirschen and B. Zhang, “Model-Free Renewable Scenario Generation Using Generative Adversarial Networks,” 2019 IEEE Power & Energy Society General Meeting (PESGM), Atlanta, GA, USA, 2019, pp. 1-1

2019

Cited alongside, same era.

S. Wang and H. Chen, “A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network,” in Applied Energy, vol. 235, pp. 1126-1140, Feb 2019

2019

Cited alongside, same era.

Y. M. Saputra, D. T. Hoang, D. N. Nguyen, E. Dutkiewicz, M. D. Mueck and S. Srikanteswara, “Energy Demand Prediction with Federated Learning for Electric Vehicle Networks,” 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, 2019, pp. 1-6

2019

Later among the works it cites.

Q. Yang, Y. Liu, T. Chen and Y. Tong, “Federated Machine Learning: Concept and Applications,” in ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 2, 2019

2019

Later among the works it cites.

F. Sattler, S. Wiedemann, K. -R. Müller and W. Samek, “Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data,” in IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 9, pp. 3400-3413, Sept. 2020

2020

Later among the works it cites.

Y. Zhan, P. Li and S. Guo, “Experience-Driven Computational Resource Allocation of Federated Learning by Deep Reinforcement Learning,” 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), New Orleans, LA, USA, 2020, pp. 234-243