  Federated Learning (FL) has emerged as a promising approach for privacy
preservation, allowing sharing of the model parameters between users and the
cloud server rather than the raw local data. FL approaches have been adopted as
a cornerstone of distributed machine learning (ML) to solve several complex use
cases. FL presents an interesting interplay between communication and ML
performance when implemented over distributed wireless nodes. Both the dynamics
of networking and learning play an important role. In this article, we
investigate the performance of FL on an application that might be used to
improve a remote healthcare system over ad hoc networks which employ CSMA/CA to
schedule its transmissions. Our FL over CSMA/CA (FLCC) model is designed to
eliminate untrusted devices and harness frequency reuse and spatial clustering
techniques to improve the throughput required for coordinating a distributed
implementation of FL in the wireless network.
  In our proposed model, frequency allocation is performed on the basis of
spatial clustering performed using virtual cells. Each cell assigns a FL server
and dedicated carrier frequencies to exchange the updated model's parameters
within the cell. We present two metrics to evaluate the network performance: 1)
probability of successful transmission while minimizing the interference, and
2) performance of distributed FL model in terms of accuracy and loss while
considering the networking dynamics.
  We benchmark the proposed approach using a well-known MNIST dataset for
performance evaluation. We demonstrate that the proposed approach outperforms
the baseline FL algorithms in terms of explicitly defining the chosen users'
criteria and achieving high accuracy in a robust network.
