  Federated learning (FL) has emerged as a distributed machine learning (ML)
technique that can protect local data privacy for participating clients and
improve system efficiency. Instead of sharing raw data, FL exchanges
intermediate learning parameters, such as gradients, among clients. This
article presents an efficient wireless communication approach tailored for FL
parameter transmission, especially for Internet of Things (IoT) devices, to
facilitate model aggregation. Our study considers practical wireless channels
that can lead to random bit errors, which can substantially affect FL
performance. Motivated by empirical gradient value distribution, we introduce a
novel received bit masking method that confines received gradient values within
prescribed limits. Moreover, given the intrinsic error resilience of ML
gradients, our approach enables the delivery of approximate gradient values
with errors without resorting to extensive error correction coding or
retransmission. This strategy reduces computational overhead at both the
transmitter and the receiver and minimizes communication latency. Consequently,
our scheme is particularly well-suited for resource-constrained IoT devices.
Additionally, we explore the inherent protection of the most significant bits
(MSBs) through gray coding in high-order modulation. Our simulations
demonstrate that our proposed scheme can effectively mitigate random bit errors
in FL performance, achieving similar learning objectives, but with the 50% air
time required by existing methods involving error correction and
retransmission.
