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In recent years, there have been great advances in the field of decentralized learning with private data.
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; Levy, O.; Lewis, M.; Zettlemoyer, L.; and Stoyanov, V. 2019 · 1907
Earlier work this paper cites.
Inverting Gradients – How easy is it to break privacy in federated learning?
Geiping, J.; Bauermeister, H.; Dröge, H.; and Moeller, M. 2020 · 2003
Earlier work this paper cites.
SplitFed: When Federated Learning Meets Split Learning
Thapa, C.; Chamikara, M. A. P.; Camtepe, S.; and Sun, L. 2021 · 2004
Earlier work this paper cites.
Language Models are Few-Shot Learners
Brown, T. B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D. M.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, A.; Sutskever, I.; and Amodei, D. 2020 · 2005
Earlier work this paper cites.
A Framework For Contrastive Self-Supervised Learning And Designing A New Approach
Falcon, W.; and Cho, K. 2020 · 2009
Earlier work this paper cites.
One weird trick for parallelizing convolutional neural networks
Krizhevsky, A. 2014 · 2014
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2015 · 2015
Earlier work this paper cites.
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K.; and Zisserman, A. 2015 · 2015
Earlier work this paper cites.
Rethinking the Inception Architecture for Computer Vision
Szegedy, C.; Vanhoucke, V.; Ioffe, S.; Shlens, J.; and Wojna, Z. 2015 · 2015
Earlier work this paper cites.
Federated Learning of Deep Networks using Model Averaging
McMahan, H. B.; Moore, E.; Ramage, D.; and y Arcas, B. A. 2016 · 2016
Cited alongside, same era.
Adam: A Method for Stochastic Optimization
Kingma, D. P.; and Ba, J. 2017 · 2017
Cited alongside, same era.
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Xiao, H.; Rasul, K.; and Vollgraf, R. 2017 · 2017
Cited alongside, same era.
The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches
Alom, M. Z.; Taha, T. M.; Yakopcic, C.; Westberg, S.; Sidike, P.; Nasrin, M. S.; Esesn, B. C. V.; Awwal, A. A. S.; and Asari, V. K. 2018 · 2018
Cited alongside, same era.
Split learning for health: Distributed deep learning without sharing raw patient data
Vepakomma, P.; Gupta, O.; Swedish, T.; and Raskar, R. 2018 · 2018
Later among the works it cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
Later among the works it cites.
Wireless Network Intelligence at the Edge
Park, J.; Samarakoon, S.; Bennis, M.; and Debbah, M. 2019 · 2019
Later among the works it cites.
AI and Compute
Dario Amodei, D. H. 2018 · 2021
Closest in time.
Larger-Scale Transformers for Multilingual Masked Language Modeling
Goyal, N.; Du, J.; Ott, M.; Anantharaman, G.; and Conneau, A. 2021 · 2021
Closest in time.
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Goyal, P.; Dollár, P.; Girshick, R.; Noordhuis, P.; Wesolowski, L.; Kyrola, A.; Tulloch, A.; Jia, Y.; and He, K. 2018 · 2018
Cited alongside, same era.
Distributed learning of deep neural network over multiple agents
Gupta, O.; and Raskar, R. 2018 · 2018
Cited alongside, same era.
Densely Connected Convolutional Networks
Huang, G.; Liu, Z.; van der Maaten, L.; and Weinberger, K. Q. 2018 · 2018
Cited alongside, same era.
Don’t Decay the Learning Rate, Increase the Batch Size
Smith, S. L.; Kindermans, P.-J.; Ying, C.; and Le, Q. V. 2018 · 2018
Cited alongside, same era.
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications
Park, J.; Samarakoon, S.; Elgabli, A.; Kim, J.; Bennis, M.; Kim, S.; and Debbah, M. 2021 · 2021
Closest in time.
Revisiting Locally Supervised Learning: an Alternative to End-to-end Training
Wang, Y.; Ni, Z.; Song, S.; Yang, L.; and Huang, G. 2021 · 2021
Closest in time.
Faithful Edge Federated Learning: Scalability and Privacy
Zhang, M.; Wei, E.; and Berry, R. 2021 · 2021
Closest in time.