  The rapid growth of Internet of Things (IoT) devices has generated vast
amounts of data, leading to the emergence of federated learning as a novel
distributed machine learning paradigm. Federated learning enables model
training at the edge, leveraging the processing capacity of edge devices while
preserving privacy and mitigating data transfer bottlenecks. However, the
conventional centralized federated learning architecture suffers from a single
point of failure and susceptibility to malicious attacks. In this study, we
delve into an alternative approach called decentralized federated learning
(DFL) conducted over a wireless mesh network as the communication backbone. We
perform a comprehensive network performance analysis using stochastic geometry
theory and physical interference models, offering fresh insights into the
convergence analysis of DFL. Additionally, we conduct system simulations to
assess the proposed decentralized architecture under various network parameters
and different aggregator methods such as FedAvg, Krum and Median methods. Our
model is trained on the widely recognized EMNIST dataset for benchmarking
handwritten digit classification. To minimize the model's size at the edge and
reduce communication overhead, we employ a cutting-edge compression technique
based on genetic algorithms. Our simulation results reveal that the compressed
decentralized architecture achieves performance comparable to the baseline
centralized architecture and traditional DFL in terms of accuracy and average
loss for our classification task. Moreover, it significantly reduces the size
of shared models over the wireless channel by compressing participants' local
model sizes to nearly half of their original size compared to the baselines,
effectively reducing complexity and communication overhead.
