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Communication constraints are one of the major challenges preventing the wide-spread adoption of Federated Learning systems.
Y. LeCun, B. Boser, J. Denker, D. Henderson, R. Howard, W. Hubbard, and L. Jackel, “Handwritten digit recognition with a back-propagation network,” in Advances in Neural Information Processing Systems (NeurIPS) , vol. 2, 1989, pp. 396–404
1989
Earlier work this paper cites.
Y. LeCun, J. S. Denker, and S. A. Solla, “Optimal brain damage,” in Advances in Neural Information Processing Systems (NeurIPS) , vol. 2, 1990, pp. 598–605
1990
Earlier work this paper cites.
D. Marpe, H. Schwarz, and T. Wiegand, “Context-based adaptive binary arithmetic coding in the H. 264/AVC video compression standard,” IEEE Trans. Circuits Syst. Video Technol. , vol. 13, no. 7, pp. 620–636, 2003
2003
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) , 2009, pp. 248–255
2009
Earlier work this paper cites.
A. Coates, A. Ng, and H. Lee, “An analysis of single-layer networks in unsupervised feature learning,” in Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS) , 2011, pp. 215–223
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems (NeurIPS) , vol. 25, 2012, pp. 1097–1105
2012
Earlier work this paper cites.
V. Vapnik, The nature of statistical learning theory . Springer Science & Business Media, 2013
2013
Earlier work this paper cites.
R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts, “Recursive deep models for semantic compositionality over a sentiment treebank,” in Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2013, pp. 1631–1642
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
M. Courbariaux, Y. Bengio, and J. David, “BinaryConnect: Training deep neural networks with binary weights during propagations,” in Advances in Neural Information Processing Systems (NeurIPS) , vol. 28, 2015, pp. 3123–3131
2015
Earlier work this paper cites.
X. Zhang, J. J. Zhao, and Y. LeCun, “Character-level convolutional networks for text classification,” in Advances in Neural Information Processing Systems (NeurIPS) , vol. 28, 2015, pp. 649–657
2015
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in 3rd International Conference on Learning Representations (ICLR) , 2015
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 770–778
2016
Earlier work this paper cites.
F. Li, B. Zhang, and B. Liu, “Ternary weight networks,” arXiv preprint arXiv:1605.04711 , 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Cited alongside, same era.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS) , 2017, pp. 1273–1282
2017
Cited alongside, same era.
A. F. Aji and K. Heafield, “Sparse communication for distributed gradient descent,” in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2017, pp. 440–445
2017
Cited alongside, same era.
K. Sayood, Introduction to data compression , 5th ed. Morgan Kaufmann, 2017
2017
Cited alongside, same era.
C. Ma, J. Li, M. Ding, H. H. Yang, F. Shu, T. Q. Quek, and H. V. Poor, “On safeguarding privacy and security in the framework of federated learning,” IEEE Netw. , vol. 34, no. 4, pp. 242–248, 2020
2020
Closest in time.
S. Niknam, H. S. Dhillon, and J. H. Reed, “Federated learning for wireless communications: Motivation, opportunities, and challenges,” IEEE Commun. Mag. , vol. 58, no. 6, pp. 46–51, 2020
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
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2017
Cited alongside, same era.
2018
Cited alongside, same era.
2019
Cited alongside, same era.
Y. Huang, Y. Cheng, A. Bapna, O. Firat, D. Chen, M. X. Chen, H. Lee, J. Ngiam, Q. V. Le, Y. Wu, and Z. Chen, “GPipe: Efficient training of giant neural networks using pipeline parallelism,” in Advances in Neural Information Processing Systems (NeurIPS) , vol. 32, 2019, pp. 103–112
2019
Cited alongside, same era.
T. Nishio and R. Yonetani, “Client selection for federated learning with heterogeneous resources in mobile edge,” in 2019 IEEE International Conference on Communications (ICC) , 2019, pp. 1–7
2019
Cited alongside, same era.
F. Sattler, S. Wiedemann, K. Müller, and W. Samek, “Sparse binary compression: Towards distributed deep learning with minimal communication,” in International Joint Conference on Neural Networks (IJCNN) , 2019, pp. 1–8
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2020
Closest in time.
P. Kidger and T. J. Lyons, “Universal approximation with deep narrow networks,” in Conference on Learning Theory (COLT) , ser. Proceedings of Machine Learning Research, vol. 125, 2020, pp. 2306–2327
2020
Closest in time.
K. F. E. Chong, “A closer look at the approximation capabilities of neural networks,” in 8th International Conference on Learning Representations (ICLR) . OpenReview.net, 2020. [Online]. Available: https://openreview.net/forum?id=rkevSgrtPr
2020
Closest in time.
F. Sattler, S. Wiedemann, K.-R. Müller, and W. Samek, “Robust and communication-efficient federated learning from non-iid data,” IEEE Trans. Neural Netw. Learn. Syst. , vol. 31, no. 9, pp. 3400–3413, 2020
2020
Closest in time.
2020
Closest in time.
D. Neumann, F. Sattler, H. Kirchhoffer, S. Wiedemann, K. Müller, H. Schwarz, T. Wiegand, D. Marpe, and W. Samek, “DeepCABAC: Plug & play compression of neural network weights and weight updates,” in 2020 IEEE International Conference on Image Processing (ICIP) , 2020, pp. 21–25
2020
Closest in time.
S. Wiedemann, H. Kirchhoffer, S. Matlage, P. Haase, A. Marbán, T. Marinc, D. Neumann, T. Nguyen, H. Schwarz, T. Wiegand, D. Marpe, and W. Samek, “DeepCABAC: A universal compression algorithm for deep neural networks,” IEEE J. Sel. Top. Signal Process. , vol. 14, no. 4, pp. 700–714, 2020
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
I. Bistritz, A. J. Mann, and N. Bambos, “Distributed distillation for on-device learning,” in 34th Conference on Neural Information Processing Systems (NeurIPS) , 2020
2020
Closest in time.
2020
Closest in time.
F. Sattler, K.-R. Müller, and W. Samek, “Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints,” IEEE Trans. Neural Netw. Learn. Syst. , pp. 1–13, 2020
2020
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