Over-the-Air Machine Learning at the Wireless Edge

By Mohammad Mohammadi Amiri, Deniz Gündüz. 2019

57 citations recorded by the source corpus.

We study distributed machine learning at the wireless edge, where limited power devices (workers) with local datasets implement distributed stochastic gradient descent (DSGD) over-the-air with the help of a remote parameter server (PS). We consider a bandwidth-limited fading multiple access channel (MAC) from the workers to the PS for communicating the local gradient estimates. Motivated by the additive nature of the wireless MAC, we study analog transmission of low-dimensional gradient estimates while accumulating error from previous iterations. We also design an opportunistic worker scheduling scheme to align the received gradient vectors at the PS in an efficient manner. Numerical results show that the proposed DSGD algorithm converges much faster than the state-of-the-art, while also providing a significantly higher accuracy.

Subfields: Artificial Intelligence, Computational Mechanics, Computer Networks and Communications

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Citations in the corpus

Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning (2020, methodology): “With channel inversion, transmissions are only allowed when |hn,i| ≥ ε, in order to avoid excessive transmit power due to the inversion [9]–[11].”
Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation (2019, methodology): “From a different perspective, many researchers have recently focused on the efficient communications between UEs and edge servers in FL-supported networks [1], [7], [19]–[24].”
Machine Learning in the Air (2019, background): “This approach can also be extended to the scenario with fading [78], [79], in which case power control can be employed at the workers to align their transmissions at the same received power level.”
Federated Learning Over Wireless Fading Channels (2019, methodology): “Here, we extend our previous works [25], [28], and study DSGD over a wireless fading MAC.”
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications (2020, background): “For this reason, it is common to allow transmissions only when the channel gains exceed a certain threshold [109]–[111].”