Fundamental limits of over-the-air optimization: Are analog schemes optimal?
This work addresses fundamental limits in distributed optimization for communication systems, providing theoretical insights with potential applications in federated learning and wireless networks, though it is incremental in refining existing bounds and schemes.
The paper tackles the problem of over-the-air convex optimization over noisy channels by deriving lower bounds on convergence rates and analyzing analog coding schemes, showing that a simple scaled transmission scheme is optimal at low SNR but suffers a slowdown factor of √d even at high SNR, while a quantize-and-modulate scheme nearly achieves optimal rates across all SNRs.
We consider over-the-air convex optimization on a $d-$dimensional space where coded gradients are sent over an additive Gaussian noise channel with variance $σ^2$. The codewords satisfy an average power constraint $P$, resulting in the signal-to-noise ratio (SNR) of $P/σ^2$. We derive bounds for the convergence rates for over-the-air optimization. Our first result is a lower bound for the convergence rate showing that any code must slowdown the convergence rate by a factor of roughly $\sqrt{d/\log(1+\mathtt{SNR})}$. Next, we consider a popular class of schemes called $analog$ $coding$, where a linear function of the gradient is sent. We show that a simple scaled transmission analog coding scheme results in a slowdown in convergence rate by a factor of $\sqrt{d(1+1/\mathtt{SNR})}$. This matches the previous lower bound up to constant factors for low SNR, making the scaled transmission scheme optimal at low SNR. However, we show that this slowdown is necessary for any analog coding scheme. In particular, a slowdown in convergence by a factor of $\sqrt{d}$ for analog coding remains even when SNR tends to infinity. Remarkably, we present a simple quantize-and-modulate scheme that uses $Amplitude$ $Shift$ $Keying$ and almost attains the optimal convergence rate at all SNRs.