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In the realm of real-world devices, centralized servers in Federated Learning (FL) present challenges including communication bottlenecks and susceptibility to a single point of failure.
Practical one-shot federated learning for cross-silo setting
Qinbin Li, Bingsheng He, and Dawn Song · 2010
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Practical one-shot federated learning for cross-silo setting
Qinbin Li, Bingsheng He, and Dawn Song · 2010
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Cyclical learning rates for training neural networks
Leslie N Smith · 2017
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Expanding the reach of federated learning by reducing client resource requirements
Sebastian Caldas, Jakub Konečny, H Brendan McMahan, and Ameet Talwalkar · 2018
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Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson · 2018
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Deep mutual learning
Ying Zhang, Tao Xiang, Timothy M Hospedales, and Huchuan Lu · 2018
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Bam! born-again multi-task networks for natural language understanding
Kevin Clark, Minh-Thang Luong, Urvashi Khandelwal, Christopher D Manning, and Quoc V Le · 2019
Earlier work this paper cites.
Fedmd: Heterogenous federated learning via model distillation
Daliang Li and Junpu Wang · 2019
Earlier work this paper cites.
Braintorrent: A peer-to-peer environment for decentralized federated learning
Abhijit Guha Roy, Shayan Siddiqui, Sebastian Pölsterl, Nassir Navab, and Christian Wachinger · 2019
Cited alongside, same era.
Heterofl: Computation and communication efficient federated learning for heterogeneous clients
Enmao Diao, Jie Ding, and Vahid Tarokh · 2020
Cited alongside, same era.
Ensemble distillation for robust model fusion in federated learning
Tao Lin, Lingjing Kong, Sebastian U Stich, and Martin Jaggi · 2020
Cited alongside, same era.
Adaptive federated optimization
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečnỳ, Sanjiv Kumar, and H Brendan McMahan · 2020
Cited alongside, same era.
Federated learning with mutually cooperating devices: A consensus approach towards server-less model optimization
Rethinking soft labels for knowledge distillation: A bias-variance tradeoff perspective
Helong Zhou, Liangchen Song, Jiajie Chen, Ye Zhou, Guoli Wang, Junsong Yuan, and Qian Zhang · 2021
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Fedrolex: Model-heterogeneous federated learning with rolling sub-model extraction
Samiul Alam, Luyang Liu, Ming Yan, and Mi Zhang · 2022
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Decentralized federated learning for nonintrusive load monitoring in smart energy communities
Alessandro Giuseppi, Sabato Manfredi, Danilo Menegatti, Antonio Pietrabissa, and Cecilia Poli · 2022
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Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
Enrique Tomás Martínez Beltrán, Mario Quiles Pérez, Pedro Miguel Sánchez Sánchez, Sergio López Bernal, Gérôme Bovet, Manuel Gil Pérez, Gregorio Martínez Pérez, and Alberto Huertas Celdrán · 2023
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Data-free one-shot federated learning under very high statistical heterogeneity
Clare Elizabeth Heinbaugh, Emilio Luz-Ricca, and Huajie Shao · 2023
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Stefano Savazzi, Monica Nicoli, Vittorio Rampa, and Sanaz Kianoush · 2020
Cited alongside, same era.
Tao Shen, Jie Zhang, Xinkang Jia, Fengda Zhang, Gang Huang, Pan Zhou, Kun Kuang, Fei Wu, and Chao Wu · 2020
Cited alongside, same era.
New insights on reducing abrupt representation change in online continual learning
Lucas Caccia, Rahaf Aljundi, Nader Asadi, Tinne Tuytelaars, Joelle Pineau, and Eugene Belilovsky · 2021
Cited alongside, same era.
A continual learning survey: Defying forgetting in classification tasks
Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Aleš Leonardis, Gregory Slabaugh, and Tinne Tuytelaars · 2021
Cited alongside, same era.
Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout
Samuel Horvath, Stefanos Laskaridis, Mario Almeida, Ilias Leontiadis, Stylianos Venieris, and Nicholas Lane · 2021
Cited alongside, same era.
Vision transformer for small-size datasets
Seung Hoon Lee, Seunghyun Lee, and Byung Cheol Song · 2021
Cited alongside, same era.
Decentralized federated learning via mutual knowledge transfer
Chengxi Li, Gang Li, and Pramod K Varshney · 2021
Cited alongside, same era.
Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith
Cited in the paper.
Later among the works it cites.
Re-weighted softmax cross-entropy to control forgetting in federated learning
Gwen Legate, Lucas Caccia, and Eugene Belilovsky · 2023
Later among the works it cites.
A comprehensive survey of continual learning: Theory, method and application
Liyuan Wang, Xingxing Zhang, Hang Su, and Jun Zhu · 2023
Later among the works it cites.
Heterogeneous federated learning: State-of-the-art and research challenges
Mang Ye, Xiuwen Fang, Bo Du, Pong C Yuen, and Dacheng Tao · 2023
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Decentralized federated learning: A survey and perspective
Liangqi Yuan, Lichao Sun, Philip S Yu, and Ziran Wang · 2023
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Enhancing one-shot federated learning through data and ensemble co-boosting
Rong Dai, Yonggang Zhang, Ang Li, Tongliang Liu, Xun Yang, and Bo Han · 2024
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