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We study federated learning (FL) at the wireless edge, where power-limited devices with local datasets collaboratively train a joint model with the help of a remote parameter server (PS).
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M. M. Amiri and D. Gündüz, “Over-the-air machine learning at the wireless edge,” in Proc. IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) , Cannes, France, Jul. 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, 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
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M. M. Amiri and D. Gündüz, “Machine learning at the wireless edge: Distributed stochastic gradient descent over-the-air,” IEEE Trans. Signal Process. , to appear
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M. M. Amiri, D. Gündüz, S. R. Kulkarni, and H. V. Poor, “Update aware device scheduling for federated learning at the wireless edge,” in Proc. IEEE Int’l Symp. on Inform. Theory (ISIT) , Los Angeles, CA, USA, Jun. 2020
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