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Personalization methods in federated learning aim to balance the benefits of federated and local training for data availability, communication cost, and robustness to client heterogeneity.
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Differentially Private Stochastic Gradient Descent for in-RDBMS Analytics
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Differentially private federated learning: A client level perspective
Robin C Geyer, Tassilo Klein, and Moin Nabi. 2017 · 2017
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Federated meta-learning with fast convergence and efficient communication
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Federated learning for mobile keyboard prediction
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Federated optimization in heterogeneous networks
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Alex Nichol, Joshua Achiam, and John Schulman. 2018 · 2018
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Applied federated learning: Improving google keyboard query suggestions
Personalized federated learning: A meta-learning approach
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar. 2020 · 2020
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Federated Multi-view Matrix Factorization for Personalized Recommendations
Adrian Flanagan, Were Oyomno, Alexander Grigorievskiy, Kuan Eeik Tan, Suleiman A Khan, and Muhammad Ammad-Ud-Din. 2020 · 2020
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Privacy Threats Against Federated Matrix Factorization
Dashan Gao, Ben Tan, Ce Ju, Vincent W Zheng, and Qiang Yang. 2020 · 2020
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Fedner: Privacy-preserving medical named entity recognition with federated learning
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Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, and Françoise Beaufays. 2018 · 2018
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Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System
Muhammad Ammad-Ud-Din, Elena Ivannikova, Suleiman A Khan, Were Oyomno, Qiang Fu, Kuan Eeik Tan, and Adrian Flanagan. 2019 · 2019
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Designing for Privacy (video and slide deck)
Apple. 2019 · 2019
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Federated learning with personalization layers
Manoj Ghuhan Arivazhagan, Vinay Aggarwal, Aaditya Kumar Singh, and Sunav Choudhary. 2019 · 2019
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Towards federated learning at scale: System design
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The Clara Training Framework Authors
NVIDIA Clara. 2019 · 2019
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The Federated Future is ready for shipping
Walter de Brouwer. 2019 · 2019
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Improving federated learning personalization via model agnostic meta learning
Yihan Jiang, Jakub Konečnỳ, Keith Rush, and Sreeram Kannan. 2019 · 2019
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Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, and Michael Moeller. 2020 · 2020
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Federated learning of a mixture of global and local models
Filip Hanzely and Peter Richtárik. 2020 · 2020
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Scaffold: Stochastic controlled averaging for federated learning. In International Conference on Machine Learning . PMLR, 5132–5143
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh. 2020 · 2020
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Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. 2020 · 2020
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Think locally, act globally: Federated learning with local and global representations
Paul Pu Liang, Terrance Liu, Liu Ziyin, Ruslan Salakhutdinov, and Louis-Philippe Morency. 2020 · 2020
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Meta Matrix Factorization for Federated Rating Predictions. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . 981–990
Yujie Lin, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Dongxiao Yu, Jun Ma, Maarten de Rijke, and Xiuzhen Cheng. 2020 · 2020
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Three approaches for personalization with applications to federated learning
Yishay Mansour, Mehryar Mohri, Jae Ro, and Ananda Theertha Suresh. 2020 · 2020
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Adaptive Federated Optimization
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečnỳ, Sanjiv Kumar, and H Brendan McMahan. 2020 · 2020
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A framework for evaluating gradient leakage attacks in federated learning
Wenqi Wei, Ling Liu, Margaret Loper, Ka-Ho Chow, Mehmet Emre Gursoy, Stacey Truex, and Yanzhao Wu. 2020 · 2020
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Salvaging federated learning by local adaptation
Tao Yu, Eugene Bagdasaryan, and Vitaly Shmatikov. 2020 · 2020
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Deep leakage from gradients
Ligeng Zhu and Song Han. 2020 · 2020
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Personalized cross-silo federated learning on non-iid data. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35. 7865–7873
Yutao Huang, Lingyang Chu, Zirui Zhou, Lanjun Wang, Jiangchuan Liu, Jian Pei, and Yong Zhang. 2021 · 2021
Closest in time.
Fedbn: Federated learning on non-iid features via local batch normalization
Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, and Qi Dou. 2021 · 2021
Closest in time.
See through Gradients: Image Batch Recovery via GradInversion
Hongxu Yin, Arun Mallya, Arash Vahdat, Jose M Alvarez, Jan Kautz, and Pavlo Molchanov. 2021 · 2021
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