Over-the-Air Machine Learning at the Wireless Edge
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[1] Yong Wang,et al. Low-Latency Broadband Analog Aggregation for Federated Edge Learning , 2018, ArXiv.
[2] Deniz Gündüz,et al. Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air , 2019, 2019 IEEE International Symposium on Information Theory (ISIT).
[3] Andrea Montanari,et al. Message-passing algorithms for compressed sensing , 2009, Proceedings of the National Academy of Sciences.
[4] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[5] Dong Yu,et al. 1-bit stochastic gradient descent and its application to data-parallel distributed training of speech DNNs , 2014, INTERSPEECH.
[6] Nikko Strom,et al. Scalable distributed DNN training using commodity GPU cloud computing , 2015, INTERSPEECH.
[7] Klaus-Robert Müller,et al. Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication , 2018, 2019 International Joint Conference on Neural Networks (IJCNN).
[8] Kenneth Heafield,et al. Sparse Communication for Distributed Gradient Descent , 2017, EMNLP.
[9] Pritish Narayanan,et al. Deep Learning with Limited Numerical Precision , 2015, ICML.
[10] Zhi Ding,et al. Federated Learning via Over-the-Air Computation , 2018, IEEE Transactions on Wireless Communications.
[11] Georgios B. Giannakis,et al. LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning , 2018, NeurIPS.
[12] Blaise Agüera y Arcas,et al. Communication-Efficient Learning of Deep Networks from Decentralized Data , 2016, AISTATS.
[13] Yann LeCun,et al. The mnist database of handwritten digits , 2005 .
[14] Deniz Gündüz,et al. Computation Scheduling for Distributed Machine Learning with Straggling Workers , 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).