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Distributed learning frameworks often rely on exchanging model parameters across workers, instead of revealing their raw data.
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2020
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S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, “Distributed federated learning for ultra-reliable low-latency vehicular communications,” IEEE Transactions on Communications , vol. 68, no. 2, pp. 1146–1159, 2020
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J. Park, S. Samarakoon, M. Bennis, and M. Debbah, “Wireless network intelligence at the edge,” Proceedings of the IEEE , vol. 107, no. 11, pp. 2204–2239, October 2019
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2019
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M. Phuong and C. Lampert, “Towards understanding knowledge distillation,” in Proc. International Conference on Machine Learning (ICML), Long Beach, CA, USA , June 2019
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B. Heo, J. Kim, S. Yun, H. Park, N. Kwak, and J. Y. Choi, “A comprehensive overhaul of feature distillation,” in International Conference on Computer Vision (ICCV) , 2019
2019
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J.-H. Ahn, O. Simeone, and J. Kang, “Wireless federated distillation for distributed edge learning with heterogeneous data,” in Proc. IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Istanbul, Turkey , September 2019
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H. Cha, J. Park, H. Kim, S.-L. Kim, and M. Bennis, “Federated reinforcement distillation with proxy experience memory,” presented atInternational Joint Conference on Artificial Intelligence (IJCAI) Workshop on Federated Machine Learning for User Privacy and Data Confidentiality (FML), Macau, China , August 2019
2019
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J. Park, S. Wang, A. Elgabli, S. Oh, E. Jeong, H. Cha, H. Kim, S.-L. Kim, and M. Bennis, “Distilling on-device intelligence at the network edge.” ArXiv preprint, arXiv: 1908.05895, 2019
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K. Lee, H. Kim, K. Lee, C. Suh, and K. Ramchandran, “Synthesizing differentially private datasets using random mixing,” in Proc. IEEE International Symposium on Information Theory (ISIT), Paris, France , July 2019
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2020
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M. Shin, C. Hwang, J. Kim, J. Park, M. Bennis, and S.-L. Kim, “XOR Mixup: Privacy-preserving data augmentation for one-shot federated learning,” presented at International Conference on Machine Learning (ICML) Workshop on Federated Learning for User Privacy and Data Confidentiality (FL-ICML), Vienna, Austria , July 2020
2020
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H. Cha, J. Park, H. Kim, M. Bennis, and S. Kim, “Proxy experience replay: Federated distillation for distributed reinforcement learning,” IEEE Intelligent Systems , vol. 35, no. 4, pp. 94–101, 2020
2020
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