Distilling On-Device Intelligence at the Network Edge

H. Lu, M.-J. Li, T. He, S. Wang, V. Narayanan, and K. S. Chan, “Robust coreset construction for distributed machine learning,” [Online]. ArXiv preprint: https://arxiv.org/abs/1904.05961

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E. Jeong, S. Oh, H. Kim, J. Park, M. Bennis, and S.-L. Kim, “Communication-efficient on-device machine learning: Federated distillation and augmentation under non-IID private data,” presented at Neural Information Processing Systems (NeurIPS) Wksp. Machine Learning on the Phone and other Consumer Devices (MLPCD) · 2018

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R. Anil, G. Pereyra, A. Passos, R. Ormandi, G. E. Dahl, and G. E. Hinton, “Large scale distributed neural network training through online distillation,” ArXiv preprint

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Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” ACM Trans. Intell. Syst. Technol

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S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, and K. Chan, “Adaptive federated learning in resource constrained edge computing systems,” IEEE Journal on Selected Areas in Communications

2019

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A. Elgabli, J. Park, A. S. Bedi, and V. Aggarwal, “GADMM: Fast and communication efficient framework for distributed machine learning,” submitted to 2019 Neural Information Processing Systems (NeurIPS)

2019

Closest in time.

H. Cha, J. Park, H. Kim, B. Bennis, and S.-L. Kim, “Federated reinforcement distillation with proxy experience memory,” to be presented at 2019 International Joint Conference on Artificial Intelligence (IJCAI) Wksp. Federated Machine Learning for User Privacy and Data Confidentiality (FML)

2019

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B. Ko, S. Wang, T. He, and D. Conway-Jones, “On data summarization for machine learning in multi-organization federations,” in in Proc. of Workshop on Distributed Analytics InfraStructure and Algorithms for Multi-Organization Federations (DAIS)

2019

Closest in time.

E. Jeong, S. Oh, J. Park, H. Kim, B. Bennis, and S.-L. Kim, “Multi-hop federated private data augmentation with sample compression,” to be presented at 2019 International Joint Conference on Artificial Intelligence (IJCAI) Wksp. Federated Machine Learning for User Privacy and Data Confidentiality (FML)