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Federated learning alleviates the privacy risk in distributed learning by transmitting only the local model updates to the central server.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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A public domain dataset for human activity recognition using smartphones
Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra Perez, and Jorge Luis Reyes Ortiz · 2013
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 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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Federated multi-task learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S Talwalkar · 2017
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2018 reform of eu data protection rules
Razvan Viorescu et al · 2017
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Leaf: A benchmark for federated settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečnỳ, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
Cited alongside, same era.
Latent weights do not exist: Rethinking binarized neural network optimization
Koen Helwegen, James Widdicombe, Lukas Geiger, Zechun Liu, Kwang-Ting Cheng, and Roeland Nusselder · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Deconstructing lottery tickets: Zeros, signs, and the supermask
Hattie Zhou, Janice Lan, Rosanne Liu, and Jason Yosinski · 2019
Cited alongside, same era.
Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 2020
Cited alongside, same era.
Pruning neural networks without any data by iteratively conserving synaptic flow
Hidenori Tanaka, Daniel Kunin, Daniel L Yamins, and Surya Ganguli · 2020
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Adaptive federated dropout: Improving communication efficiency and generalization for federated learning
Nader Bouacida, Jiahui Hou, Hui Zang, and Xin Liu · 2021
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HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients
Enmao Diao, Jie Ding, and Vahid Tarokh · 2021
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Ensemble attention distillation for privacy-preserving federated learning
Xuan Gong, Abhishek Sharma, Srikrishna Karanam, Ziyan Wu, Terrence Chen, David Doermann, and Arun Innanje · 2021
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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
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Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 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.
What’s hidden in a randomly weighted neural network?
Vivek Ramanujan, Mitchell Wortsman, Aniruddha Kembhavi, Ali Farhadi, and Mohammad Rastegari · 2020
Cited alongside, same era.
Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization
Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie, and Ramtin Pedarsani · 2020
Cited alongside, same era.
Fedmask: Joint computation and communication-efficient personalized federated learning via heterogeneous masking
Ang Li, Jingwei Sun, Xiao Zeng, Mi Zhang, Hai Li, and Yiran Chen · 2021
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Data-free knowledge distillation for heterogeneous federated learning
Zhuangdi Zhu, Junyuan Hong, and Jiayu Zhou · 2021
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Peek-a-boo: What (more) is disguised in a randomly weighted neural network, and how to find it efficiently
Xiaohan Chen, Jason Zhang, and Zhangyang Wang · 2022
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