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Distributed deep learning frameworks like federated learning (FL) and its variants are enabling personalized experiences across a wide range of web clients and mobile/IoT devices.
Continual Learning via Neural Pruning
Siavash Golkar, Michael Kagan, and Kyunghyun Cho. 2019 · 1903
Earlier work this paper cites.
Shredder: Learning Noise to Protect Privacy with Partial DNN Inference on the Edge
Fatemehsadat Mireshghallah, Mohammadkazem Taram, Prakash Ramrakhyani, Dean M. Tullsen, and Hadi Esmaeilzadeh. 2019 · 1905
Earlier work this paper cites.
DeepObfuscator: Adversarial Training Framework for Privacy-Preserving Image Classification
Ang Li, Jiayi Guo, Huanrui Yang, and Yiran Chen. 2019 · 1909
Earlier work this paper cites.
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, and Ananda Theertha Suresh. 2021 · 1910
Earlier work this paper cites.
Data parallel algorithms
W Daniel Hillis and Guy L Steele Jr. 1986 · 1986
Earlier work this paper cites.
Optimal Brain Damage. In Advances in Neural Information Processing Systems , D. Touretzky (Ed.), Vol. 2. Morgan-Kaufmann
Yann LeCun, John Denker, and Sara Solla. 1990 · 1989
Earlier work this paper cites.
Principles of risk minimization for learning theory. In Advances in neural information processing systems . 831–838
Vladimir Vapnik. 1992 · 1992
Earlier work this paper cites.
Using Confidence Bounds for Exploitation-Exploration Trade-Offs
Peter Auer. 2003 · 2003
Earlier work this paper cites.
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H. Vincent Poor. 2020 · 2007
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky. 2009 · 2009
Earlier work this paper cites.
MNIST handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges. 2010 · 2010
Earlier work this paper cites.
notMNIST dataset
Yaroslav Bulatov. 2011 · 2011
Earlier work this paper cites.
Large scale distributed deep networks
Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Marc’aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, et al · 2012
Earlier work this paper cites.
Sparsifying Neural Network Connections for Face Recognition
Yi Sun, Xiaogang Wang, and Xiaoou Tang. 2015 · 2015
Earlier work this paper cites.
Revisiting distributed synchronous SGD
Jianmin Chen, Xinghao Pan, Rajat Monga, Samy Bengio, and Rafal Jozefowicz. 2016 · 2016
Earlier work this paper cites.
Distributed deep learning using synchronous stochastic gradient descent
Dipankar Das, Sasikanth Avancha, Dheevatsa Mudigere, Karthikeyan Vaidynathan, Srinivas Sridharan, Dhiraj Kalamkar, Bharat Kaul, and Pradeep Dubey. 2016 · 2016
Earlier work this paper cites.
Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon. 2016 · 2016
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data (2016)
H Brendan McMahan, Eider Moore, Daniel Ramage, S Hampson, and BA Arcas. 2016b · 2016
Earlier work this paper cites.
Federated Learning of Deep Networks using Model Averaging
H. Brendan McMahan, Eider Moore, Daniel Ramage, and Blaise Agüera y Arcas. 2016a · 2016
Earlier work this paper cites.
Improved Deep Metric Learning with Multi-class N-pair Loss Objective. In Advances in Neural Information Processing Systems , D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett (Eds.), Vol. 29. Curran Associates, Inc
Kihyuk Sohn. 2016 · 2016
Earlier work this paper cites.
A survey of model compression and acceleration for deep neural networks
Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang. 2017 · 2017
Earlier work this paper cites.
The EU General Data Protection Regulation (GDPR): European regulation that has a global impact
Michelle Goddard. 2017 · 2017
Earlier work this paper cites.
ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression
Jian-Hao Luo, Jianxin Wu, and Weiyao Lin. 2017 · 2017
Earlier work this paper cites.
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf. 2017 · 2017
Earlier work this paper cites.
Tal Ben-Nun. 2018 · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Earlier work this paper cites.
Distributed learning of deep neural network over multiple agents
Otkrist Gupta and Ramesh Raskar. 2018 · 2018
Earlier work this paper cites.
Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage. 2018 · 2018
Earlier work this paper cites.
Pipedream: Fast and efficient pipeline parallel dnn training
Aaron Harlap, Deepak Narayanan, Amar Phanishayee, Vivek Seshadri, Nikhil Devanur, Greg Ganger, and Phil Gibbons. 2018 · 2018
Earlier work this paper cites.
Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks
Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, and Nathan Srebro. 2018 · 2018
Earlier work this paper cites.
Mesh-tensorflow: Deep learning for supercomputers
Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Koanantakool, Peter Hawkins, HyoukJoong Lee, Mingsheng Hong, Cliff Young, et al · 2018
Earlier work this paper cites.
Split learning for health: Distributed deep learning without sharing raw patient data
Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, and Ramesh Raskar. 2018a · 2018
Cited alongside, same era.
Split learning for health: Distributed deep learning without sharing raw patient data
Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, and Ramesh Raskar. 2018b · 2018
Cited alongside, same era.
No peek: A survey of private distributed deep learning
Praneeth Vepakomma, Tristan Swedish, Ramesh Raskar, Otkrist Gupta, and Abhimanyu Dubey. 2018c · 2018
Cited alongside, same era.
Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra. 2018 · 2018
Cited alongside, same era.
Fedboost: A communication-efficient algorithm for federated learning. In International Conference on Machine Learning . PMLR, 3973–3983
Jenny Hamer, Mehryar Mohri, and Ananda Theertha Suresh. 2020 · 2020
Later among the works it cites.
Fedml: A research library and benchmark for federated machine learning
Chaoyang He, Songze Li, Jinhyun So, Xiao Zeng, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, et al · 2020
Later among the works it cites.
Multiple Classification with Split Learning
Jongwon Kim, Sungho Shin, Yeonguk Yu, Junseok Lee, and Kyoobin Lee. 2020 · 2020
Later among the works it cites.
Distributed heteromodal split learning for vision aided mmWave received power prediction
Yusuke Koda, Jihong Park, Mehdi Bennis, Koji Yamamoto, Takayuki Nishio, and Masahiro Morikura. 2020a · 2020
Later among the works it cites.
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Tal Ben-Nun and Torsten Hoefler. 2019 · 2019
Cited alongside, same era.
Adversarially learned representations for information obfuscation and inference. In International Conference on Machine Learning . PMLR, 614–623
Martin Bertran, Natalia Martinez, Afroditi Papadaki, Qiang Qiu, Miguel Rodrigues, Galen Reeves, and Guillermo Sapiro. 2019 · 2019
Cited alongside, same era.
Model inversion attacks against collaborative inference. In Proceedings of the 35th Annual Computer Security Applications Conference . 148–162
Zecheng He, Tianwei Zhang, and Ruby B Lee. 2019 · 2019
Cited alongside, same era.
Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
Cited alongside, same era.
Fedmd: Heterogenous federated learning via model distillation
Daliang Li and Junpu Wang. 2019 · 2019
Cited alongside, same era.
Privacy adversarial network: representation learning for mobile data privacy
Sicong Liu, Junzhao Du, Anshumali Shrivastava, and Lin Zhong. 2019 · 2019
Cited alongside, same era.
Putting an end to end-to-end: Gradient-isolated learning of representations
Sindy Löwe, Peter O’Connor, and Bastiaan S Veeling. 2019 · 2019
Cited alongside, same era.
Client selection for federated learning with heterogeneous resources in mobile edge. In ICC 2019-2019 IEEE International Conference on Communications (ICC) . IEEE, 1–7
Takayuki Nishio and Ryo Yonetani. 2019 · 2019
Cited alongside, same era.
Communication-efficient multimodal split learning for mmWave received power prediction
Yusuke Koda, Jihong Park, Mehdi Bennis, Koji Yamamoto, Takayuki Nishio, Masahiro Morikura, and Kota Nakashima. 2020b · 2020
Later among the works it cites.
Parallel Training of Deep Networks with Local Updates
Michael Laskin, Luke Metz, Seth Nabarrao, Mark Saroufim, Badreddine Noune, Carlo Luschi, Jascha Sohl-Dickstein, and Pieter Abbeel. 2020 · 2020
Later among the works it cites.
Gshard: Scaling giant models with conditional computation and automatic sharding
Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, and Zhifeng Chen. 2020 · 2020
Later among the works it cites.
Federated Optimization in Heterogeneous Networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020 · 2020
Later among the works it cites.
A hybrid deep learning architecture for privacy-preserving mobile analytics
Seyed Ali Osia, Ali Shahin Shamsabadi, Sina Sajadmanesh, Ali Taheri, Kleomenis Katevas, Hamid R Rabiee, Nicholas D Lane, and Hamed Haddadi. 2020 · 2020
Later among the works it cites.
Unleashing the Tiger: Inference Attacks on Split Learning
Dario Pasquini, Giuseppe Ateniese, and Massimo Bernaschi. 2020 · 2020
Later among the works it cites.
The future of digital health with federated learning
Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletari, Holger R Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N Galtier, Bennett A Landman, Klaus Maier-Hein, et al · 2020
Later among the works it cites.
Private Split Inference of Deep Networks
Mohammad Samragh, Hossein Hosseini, Kambiz Azarian, and Joseph Soriaga. 2020 · 2020
Later among the works it cites.
Handling of Personal Information and Deidentified, Aggregated, and Pseudonymized Information Under the California Consumer Privacy Act
William Stallings. 2020 · 2020
Later among the works it cites.
Splitfed: When federated learning meets split learning
Chandra Thapa, Mahawaga Arachchige Pathum Chamikara, and Seyit Camtepe. 2020 · 2020
Later among the works it cites.
NoPeek: Information leakage reduction to share activations in distributed deep learning
Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta, and Ramesh Raskar. 2020 · 2020
Later among the works it cites.
On Large-Cohort Training for Federated Learning
Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, and Virginia Smith. 2021 · 2021
Closest in time.
Massively Parallel and Dynamic Algorithms for Minimum Size Clustering
Alessandro Epasto, Mohammad Mahdian, Vahab Mirrokni, and Peilin Zhong. 2021 · 2021
Closest in time.
Comparison of privacy-preserving distributed deep learning methods in healthcare. In Annual Conference on Medical Image Understanding and Analysis . Springer, 457–471
Manish Gawali, CS Arvind, Shriya Suryavanshi, Harshit Madaan, Ashrika Gaikwad, KN Bhanu Prakash, Viraj Kulkarni, and Aniruddha Pant. 2021 · 2021
Closest in time.
Training speech recognition models with federated learning: A quality/cost framework. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 3080–3084
Dhruv Guliani, Françoise Beaufays, and Giovanni Motta. 2021 · 2021
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Vulnerability Due to Training Order in Split Learning
Harshit Madaan, Manish Gawali, Viraj Kulkarni, and Aniruddha Pant. 2021 · 2021
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FEDZIP: A Compression Framework for Communication-Efficient Federated Learning
Amirhossein Malekijoo, Mohammad Javad Fadaeieslam, Hanieh Malekijou, Morteza Homayounfar, Farshid Alizadeh-Shabdiz, and Reza Rawassizadeh. 2021 · 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 · 2021
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SplitEasy: A Practical Approach for Training ML models on Mobile Devices. In Proceedings of the 22nd International Workshop on Mobile Computing Systems and Applications . 37–43
Kamalesh Palanisamy, Vivek Khimani, Moin Hussain Moti, and Dimitris Chatzopoulos. 2021 · 2021
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Communication-efficient and distributed learning over wireless networks: Principles and applications
Jihong Park, Sumudu Samarakoon, Anis Elgabli, Joongheon Kim, Mehdi Bennis, Seong-Lyun Kim, and Mérouane Debbah. 2021 · 2021
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DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 12125–12135
Abhishek Singh, Ayush Chopra, Ethan Garza, Emily Zhang, Praneeth Vepakomma, Vivek Sharma, and Ramesh Raskar. 2021 · 2021
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Advancements of federated learning towards privacy preservation: from federated learning to split learning
Chandra Thapa, Mahawaga Arachchige Pathum Chamikara, and Seyit A Camtepe. 2021 · 2021
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Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer. 2021 · 2021
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AdaptCL: Efficient Collaborative Learning with Dynamic and Adaptive Pruning
Guangmeng Zhou, Ke Xu, Qi Li, Yang Liu, and Yi Zhao. 2021 · 2021
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Federated Learning on Non-IID Data: A Survey
Hangyu Zhu, Jinjin Xu, Shiqing Liu, and Yaochu Jin. 2021 · 2021
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Hermes: An Efficient Federated Learning Framework for Heterogeneous Mobile Clients
Ang Li, Jingwei Sun, Pengcheng Li, Yu Pu, Hai Li, and Yiran Chen. 2022 · 2022
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