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Personalized federated learning, as a variant of federated learning, trains customized models for clients using their heterogeneously distributed data.
Edge Intelligence: the Confluence of Edge Computing and Artificial Intelligence
Shuiguang Deng, Hailiang Zhao, Jianwei Yin, Schahram Dustdar, and Albert Y. Zomaya · 1909
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Proximal alternating minimization and projection methods for nonconvex problems: An approach based on the kurdyka-łojasiewicz inequality
Hédy Attouch, Jérôme Bolte, Patrick Redont, and Antoine Soubeyran · 2010
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Robust pca via outlier pursuit
Huan Xu, Constantine Caramanis, and Sujay Sanghavi · 2010
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Robust principal component analysis?
Emmanuel J Candès, Xiaodong Li, Yi Ma, and John Wright · 2011
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Learning efficient sparse and low rank models
Pablo Sprechmann, Alexander M Bronstein, and Guillermo Sapiro · 2015
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Practical secure aggregation for federated learning on user-held data
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2016
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Mini-batch stochastic approximation methods for nonconvex stochastic composite optimization
Saeed Ghadimi, Guanghui Lan, and Hongchao Zhang · 2016
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Linear convergence of gradient and proximal-gradient methods under the polyak-łojasiewicz condition
Hamed Karimi, Julie Nutini, and Mark Schmidt · 2016
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Douglas–rachford splitting for nonconvex optimization with application to nonconvex feasibility problems
Guoyin Li and Ting Kei Pong · 2016
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Communication-efficient learning of deep networks from decentralized data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al · 2016
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On compressing deep models by low rank and sparse decomposition
Xiyu Yu, Tongliang Liu, Xinchao Wang, and Dacheng Tao · 2017
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How to backdoor federated learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov · 2018
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Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2018
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A simple proximal stochastic gradient method for nonsmooth nonconvex optimization
Zhize Li and Jian Li · 2018
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Convergence of multi-block bregman admm for nonconvex composite problems
Fenghui Wang, Wenfei Cao, and Zongben Xu · 2018
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Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2018
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Federated learning with personalization layers
Manoj Ghuhan Arivazhagan, Vinay Aggarwal, Aaditya Kumar Singh, and Sunav Choudhary · 2019
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Measuring the effects of non-identical data distribution for federated visual classification
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2019
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On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2019
Cited alongside, same era.
Robust aggregation for federated learning
Krishna Pillutla, Sham M Kakade, and Zaid Harchaoui · 2019
Cited alongside, same era.
Dba: Distributed backdoor attacks against federated learning
Chulin Xie, Keli Huang, Pin-Yu Chen, and Bo Li · 2019
Cited alongside, same era.
Yae Jee Cho, Jianyu Wang, and Gauri Joshi · 2020
Cited alongside, same era.
Adaptive personalized federated learning, 2020
Yuyang Deng, Mohammad Mahdi Kamani, and Mehrdad Mahdavi · 2020
Cited alongside, same era.
Exploiting shared representations for personalized federated learning
Liam Collins, Hamed Hassani, Aryan Mokhtari, and Sanjay Shakkottai · 2021
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Flix: A simple and communication-efficient alternative to local methods in federated learning
Elnur Gasanov, Ahmed Khaled, Samuel Horváth, and Peter Richtárik · 2021
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What doesn’t kill you makes you robust (er): Adversarial training against poisons and backdoors
Jonas Geiping, Liam Fowl, Gowthami Somepalli, Micah Goldblum, Michael Moeller, and Tom Goldstein · 2021
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Stochastic proximal methods for non-smooth non-convex constrained sparse optimization
Michael R Metel and Akiko Takeda · 2021
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Ppfl: privacy-preserving federated learning with trusted execution environments
Fan Mo, Hamed Haddadi, Kleomenis Katevas, Eduard Marin, Diego Perino, and Nicolas Kourtellis · 2021
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Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 2020
Cited alongside, same era.
An efficiency-boosting client selection scheme for federated learning with fairness guarantee
Tiansheng Huang, Weiwei Lin, Wentai Wu, Ligang He, Keqin Li, and Albert Y Zomaya · 2020
Cited alongside, same era.
Low-rank compression of neural nets: Learning the rank of each layer
Yerlan Idelbayev and Miguel A Carreira-Perpinán · 2020
Cited alongside, same era.
Scaffold: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
Cited alongside, same era.
Ang Li, Jingwei Sun, Binghui Wang, Lin Duan, Sicheng Li, Yiran Chen, and Hai Li · 2020
Cited alongside, same era.
Think locally, act globally: Federated learning with local and global representations
Paul Pu Liang, Terrance Liu, Liu Ziyin, Nicholas B Allen, Randy P Auerbach, David Brent, Ruslan Salakhutdinov, and Louis-Philippe Morency · 2020
Cited alongside, same era.
Three approaches for personalization with applications to federated learning
Yishay Mansour, Mehryar Mohri, Jae Ro, and Ananda Theertha Suresh · 2020
Cited alongside, same era.
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Crfl: Certifiably robust federated learning against backdoor attacks
Chulin Xie, Minghao Chen, Pin-Yu Chen, and Bo Li · 2021
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Fedcm: Federated learning with client-level momentum
Jing Xu, Sen Wang, Liwei Wang, and Andrew Chi-Chih Yao · 2021
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Fedpd: A federated learning framework with adaptivity to non-iid data
Xinwei Zhang, Mingyi Hong, Sairaj Dhople, Wotao Yin, and Yang Liu · 2021
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Dispfl: Towards communication-efficient personalized federated learning via decentralized sparse training
Rong Dai, Li Shen, Fengxiang He, Xinmei Tian, and Dacheng Tao · 2022
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Fedadmm: A robust federated deep learning framework with adaptivity to system heterogeneity
Yonghai Gong, Yichuan Li, and Nikolaos M Freris · 2022
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Wonyong Jeong and Sung Ju Hwang · 2022
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Personalized federated learning with robust clustering against model poisoning
Jie Ma, Ming Xie, and Guodong Long · 2022
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Federated learning with partial model personalization
Krishna Pillutla, Kshitiz Malik, Abdel-Rahman Mohamed, Mike Rabbat, Maziar Sanjabi, and Lin Xiao · 2022
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Lightsecagg: a lightweight and versatile design for secure aggregation in federated learning
Jinhyun So, Corey J Nolet, Chien-Sheng Yang, Songze Li, Qian Yu, Ramy E Ali, Basak Guler, and Salman Avestimehr · 2022
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Fedadmm: A federated primal-dual algorithm allowing partial participation
Han Wang, Siddartha Marella, and James Anderson · 2022
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Robust weight perturbation for adversarial training
Chaojian Yu, Bo Han, Mingming Gong, Li Shen, Shiming Ge, Bo Du, and Tongliang Liu · 2022
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Fedabc: Targeting fair competition in personalized federated learning
Dui Wang, Li Shen, Yong Luo, Han Hu, Kehua Su, Yonggang Wen, and Dacheng Tao · 2023
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