Performance Analysis for Resource Constrained Decentralized Federated
  Learning Over Wireless Networks
Zhigang Yan Dong Li
Federated learning (FL) can lead to significant communication overhead and
reliance on a central server. To address these challenges, decentralized
federated learning (DFL) has been proposed as a more resilient framework. DFL
involves parameter exchange between devices through a wireless network. This
study analyzes the performance of resource-constrained DFL using different
communication schemes (digital and analog) over wireless networks to optimize
communication efficiency. Specifically, we provide convergence bounds for both
digital and analog transmission approaches, enabling analysis of the model
performance trained on DFL. Furthermore, for digital transmission, we
investigate and analyze resource allocation between computation and
communication and convergence rates, obtaining its communication complexity and
the minimum probability of correction communication required for convergence
guarantee. For analog transmission, we discuss the impact of channel fading and
noise on the model performance and the maximum errors accumulation with
convergence guarantee over fading channels. Finally, we conduct numerical
simulations to evaluate the performance and convergence rate of convolutional
neural networks (CNNs) and Vision Transformer (ViT) trained in the DFL
framework on fashion-MNIST and CIFAR-10 datasets. Our simulation results
validate our analysis and discussion, revealing how to improve performance by
optimizing system parameters under different communication conditions.