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This paper addresses the problem of decentralized learning to achieve a high-performance global model by asking a group of clients to share local models pre-trained with their own data resources.
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A. Tarvainen and H. Valpola, “Mean Teachers are Better Role Models: Weight-Averaged Consistency Targets Improve Semi-supervised Deep Learning Results,” in Conference on Neural Information Processing Systems , 2017, pp. 1195–1204
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S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, and K. Chan, “When Edge Meets Learning: Adaptive Control for Resource-Constrained Distributed Machine Learning,” in IEEE International Conference on Communications , 2018
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2017
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T. Ching, D. S. Himmelstein, B. K. Beaulieu-Jones, A. A. Kalinin, B. T. Do, G. P. Way, E. Ferrero, P. M. Agapow, M. Zietz, M. M. Hoffman, W. Xie, G. L. Rosen, B. J. Lengerich, J. Israeli, J. Lanchantin, S. Woloszynek, A. E. Carpenter, A. Shrikumar, J. Xu, E. M. Cofer, C. A. Lavender, S. C. Turaga, A. M. Alexandari, Z. Lu, D. J. Harris, D. DeCaprio, Y. Qi, A. Kundaje, Y. Peng, L. K. Wiley, M. H. S. Segler, S. M. Boca, S. J. Swamidass, A. Huang, A. Gitter, and C. S. Greene, “Opportunities and Obstacles for Deep Learning in Biology and Medicine,” Journal of The Royal Society Interface , vol. 15, no. 141, 2018
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Y. Lin, S. Han, H. Mao, Y. Wang, and W. J. Dally, “Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training,” in International Conference on Learning Representations , 2018
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I. Radosavovic, P. Dollár, R. Girshick, G. Gkioxari, and K. He, “Data Distillation: Towards Omni-supervised Learning,” in IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 4119–4128
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2019
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W. Li, F. Milletarì, D. Xu, N. Rieke, J. Hancox, W. Zhu, M. Baust, Y. Cheng, S. Ourselin, M. J. Cardoso, and A. Feng, “Privacy-Preserving Federated Brain Tumour Segmentation,” in International Workshop on Machine Learning in Medical Imaging , 2019, pp. 133–141
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T. Nishio and R. Yonetani, “Client selection for federated learning with heterogeneous resources in mobile edge,” in IEEE International Conference on Communications , 2019, pp. 1–7
2019
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J.-H. Ahn, O. Simeone, and J. Kang, “Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Data,” in IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications , 2019, pp. 1–6
2019
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2019
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