  In this paper, we propose an over-the-air (OTA)-based approach for
distributed matrix-vector multiplications in the context of distributed machine
learning (DML). Thanks to OTA computation, the column-wise partitioning of a
large matrix enables efficient workload distribution among workers (i.e., local
computing nodes) based on their computing capabilities. In addition, without
requiring additional bandwidth, it allows the system to remain scalable even as
the number of workers increases to mitigate the impact of slow workers, known
as stragglers. However, despite the improvements, there are still instances
where some workers experience deep fading and become stragglers, preventing
them from transmitting their results. By analyzing the mean squared error
(MSE), we demonstrate that incorporating more workers in the OTA-based approach
leads to MSE reduction without the need for additional radio resources.
Furthermore, we introduce an analog coding scheme to further enhance the
performance and compare it with conventional coded multiplication (CM) schemes.
Through simulations, it is shown that the OTA-based approach achieves
comparable performance to CM schemes while potentially requiring fewer radio
resources.
