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Federated Learning (FL) emerged as a practical approach to training a model from decentralized data.
Deep Learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
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Federated optimization: Distributed machine learning for on-device intelligence
Jakub Konečnỳ, H Brendan McMahan, Daniel Ramage, and Peter Richtárik · 2016
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Federated learning of deep networks using model averaging
H. Brendan McMahan, Eider Moore, Daniel Ramage, and Blaise Agüera y Arcas · 2016
Earlier work this paper cites.
Hierarchical federated learning across heterogeneous cellular networks
M. S. H. Abad, E. Ozfatura, D. GUndUz, and O. Ercetin · 2020
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Mitigating bias in federated learning
Annie Abay, Yi Zhou, Nathalie Baracaldo, Shashank Rajamoni, Ebube Chuba, and Heiko Ludwig · 2020
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Federated learning in smart city sensing: Challenges and opportunities
Ji Chu Jiang, Burak Kantarci, Sema Oktug, and Tolga Soyata · 2020
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SCAFFOLD: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
Earlier work this paper cites.
Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
Earlier work this paper cites.
Fair resource allocation in federated learning
Tian Li, Maziar Sanjabi, Ahmad Beirami, and Virginia Smith · 2020
Earlier work this paper cites.
Adaptive federated optimization
Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, and H. Brendan McMahan · 2020
Cited alongside, same era.
Tackling the objective inconsistency problem in heterogeneous federated optimization
Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H. Vincent Poor · 2020
Cited alongside, same era.
Why gradient clipping accelerates training: A theoretical justification for adaptivity
Jingzhao Zhang, Tianxing He, Suvrit Sra, and Ali Jadbabaie · 2020
Cited alongside, same era.
Federated learning based on dynamic regularization
Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro, Matthew Mattina, Paul N Whatmough, and Venkatesh Saligrama · 2021
Cited alongside, same era.
On large-cohort training for federated learning
Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, and Virginia Smith · 2021
Characterizing impacts of heterogeneity in federated learning upon large-scale smartphone data
Chengxu Yang, Qipeng Wang, Mengwei Xu, Zhenpeng Chen, Kaigui Bian, Yunxin Liu, and Xuanzhe Liu · 2021
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Feddc: Federated learning with non-iid data via local drift decoupling and correction
Liang Gao, Huazhu Fu, Li Li, Yingwen Chen, Ming Xu, and Cheng-Zhong Xu · 2022
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Federated learning with buffered asynchronous aggregation
John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Mike Rabbat, Mani Malek, and Dzmitry Huba · 2022
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Fedbalancer: Data and pace control for efficient federated learning on heterogeneous clients
Jaemin Shin, Yuanchun Li, Yunxin Liu, and Sung-Ju Lee · 2022
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Refl: Resource-efficient federated learning
Ahmed M. Abdelmoniem, Atal Narayan Sahu, Marco Canini, and Suhaib A. Fahmy · 2023
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Cited alongside, same era.
Fedfair: Training fair models in cross-silo federated learning
Lingyang Chu, Lanjun Wang, Yanjie Dong, Jian Pei, Zirui Zhou, and Yong Zhang · 2021
Cited alongside, same era.
Fairfed: Enabling group fairness in federated learning
Yahya H Ezzeldin, Shen Yan, Chaoyang He, Emilio Ferrara, and Salman Avestimehr · 2021
Cited alongside, same era.
Fedscale: Benchmarking model and system performance of federated learning
Fan Lai, Yinwei Dai, Xiangfeng Zhu, and Mosharaf Chowdhury · 2021
Cited alongside, same era.
Oort: Efficient federated learning via guided participant selection
Fan Lai, Xiangfeng Zhu, Harsha V. Madhyastha, and Mosharaf Chowdhury · 2021
Cited alongside, same era.
Flame: Simplifying topology extension in federated learning
Harshit Daga, Jaemin Shin, Dhruv Garg, Ada Gavrilovska, Myungjin Lee, and Ramana Rao Kompella · 2023
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Mohawk: Mobility and heterogeneity-aware dynamic community selection for hierarchical federated learning
Allen-Jasmin Farcas, Myungjin Lee, Ramana Rao Kompella, Hugo Latapie, Gustavo de Veciana, and Radu Marculescu · 2023
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A survey on federated learning systems: Vision, hype and reality for data privacy and protection
Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu, and Bingsheng He · 2023
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Mitigating group bias in federated learning: Beyond local fairness, 2023
Ganghua Wang, Ali Payani, Myungjin Lee, and Ramana Kompella · 2023
Later among the works it cites.