The following publications are possibly variants of this publication:
- Inverting Gradients - How easy is it to break privacy in federated learning?Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, Michael Moeller 0001. nips 2020: [doi]
- Energy Disaggregation with Federated and Transfer LearningQi Li, Jin Ye, WenZhan Song 0001, Zion Tse. wf-iot 2021: 698-703 [doi]
- Privacy-Preserving Federated Learning based on Differential Privacy and Momentum Gradient DescentShangyin Weng, Lei Zhang 0035, Daquan Feng, Chenyuan Feng, Ruiyu Wang, Paulo Valente Klaine, Muhammad Ali Imran 0001. ijcnn 2022: 1-6 [doi]
- A Privacy-Preserving Federated Learning Method for Probabilistic Community-Level Behind-the-Meter Solar Generation DisaggregationJun Lin, Jin Ma, Jianguo Zhu 0001. tsg, 13(1):268-279, 2022. [doi]
- Efficient and Privacy-Preserving Federated Learning with Irregular UsersJieyu Xu, Hongwei Li 0001, Jia Zeng, Meng Hao. icc 2022: 534-539 [doi]