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We study federated machine learning (ML) at the wireless edge, where power- and bandwidth-limited wireless devices with local datasets carry out distributed stochastic gradient descent (DSGD) with the help of a remote parameter server (PS).
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M. M. Amiri and D. Gündüz, “Machine learning at the wireless edge: Distributed stochastic gradient descent over-the-air,” in Proc. IEEE Int’l Symp. on Inform. Theory (ISIT) , Paris, France, Jul. 2019, pp. 1432–1436
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M. M. Amiri, T. M. Duman, and D. Gündüz, “Collaborative machine learning at the wireless edge with blind transmitters,” in Proc. IEEE Global Conference on Signal and Information Processing (GlobalSIP) , Ottawa, ON, Canada, Nov. 2019, pp. 1–5
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G. Zhu, Y. Wang, and K. Huang, “Broadband analog aggregation for low-latency federated edge learning,” IEEE Trans. Wireless Commun. , vol. 19, no. 1, pp. 491–506, Jan. 2020
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M. M. Amiri and D. Gündüz, “Federated learning over wireless fading channels,” IEEE Trans. Wireless Commun. , Early Access, Feb. 2020
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