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In current deep learning paradigms, local training or the Standalone framework tends to result in overfitting and thus poor generalizability.
“Privacy-preserving deep learning,”
Reza Shokri and Vitaly Shmatikov, · 2008
Earlier work this paper cites.
“Communication-efficient learning of deep networks from decentralized data,”
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas, · 2017
Earlier work this paper cites.
“On designing data quality-aware truth estimation and surplus sharing method for mobile crowdsensing,”
Shuo Yang, Fan Wu, Shaojie Tang, Xiaofeng Gao, Bo Yang, and Guihai Chen, · 2017
Earlier work this paper cites.
“Profit sharing and efficiency in utility games,”
Sreenivas Gollapudi, Kostas Kollias, Debmalya Panigrahi, and Venetia Pliatsika, · 2017
Earlier work this paper cites.
“Differentially private fair learning,”
Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, and Jonathan Ullman, · 2018
Earlier work this paper cites.
“Advances and open problems in federated learning,”
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al., · 2019
Cited alongside, same era.
“Federated machine learning: Concept and applications,”
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong, · 2019
Cited alongside, same era.
“Federated learning: Challenges, methods, and future directions,”
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith, · 2019
Cited alongside, same era.
Federated Learning
Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, and Han Yu, · 2019
Cited alongside, same era.
“On the compatibility of privacy and fairness,” 2019
Rachel Cummings, Varun Gupta, Dhamma Kimpara, and Jamie Morgenstern, · 2019
Cited alongside, same era.
“Rewarding high-quality data via influence functions,”
Adam Richardson, Aris Filos-Ratsikas, and Boi Faltings, · 2019
Later among the works it cites.
“A fairness-aware incentive scheme for federated learning,”
Han Yu, Zelei Liu, Yang Liu, Tianjian Chen, Mingshu Cong, Xi Weng, Dusit Niyato, and Qiang Yang, · 2020
Closest in time.
“Threats to federated learning: A survey,”
Lingjuan Lyu, Han Yu, and Qiang Yang, · 2020
Closest in time.
“Towards fair and privacy-preserving federated deep models,”
Lingjuan Lyu, Jiangshan Yu, Karthik Nandakumar, Yitong Li, Xingjun Ma, Jiong Jin, Han Yu, and Kee Siong Ng, · 2020
Closest in time.
“How to democratise and protect ai: Fair and differentially private decentralised deep learning,”
Lingjuan Lyu, Yitong Li, Karthik Nandakumar, Jiangshan Yu, and Xingjun Ma, · 2020
Closest in time.
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