Authors: Hunmin Lee, Hongju Seong, Wonbin Kim, Hyeokchan Kwon, Daehee Seo
Abstract: Automatic modulation classification (AMC) serves a vital role in ensuring
efficient and reliable communication services within distributed wireless
networks. Recent developments have seen a surge in interest in deep neural
network (DNN)-based AMC models, with Federated Learning (FL) emerging as a
promising framework. Despite these advancements, the presence of various noises
within the signal exerts significant challenges while optimizing models to
capture salient features. Furthermore, existing FL-based AMC models commonly
rely on linear aggregation strategies, which face notable difficulties in
integrating locally fine-tuned parameters within practical non-IID (Independent
and Identically Distributed) environments, thereby hindering optimal learning
convergence. To address these challenges, we propose FedVaccine, a novel FL
model aimed at improving generalizability across signals with varying noise
levels by deliberately introducing a balanced level of noise. This is
accomplished through our proposed harmonic noise resilience approach, which
identifies an optimal noise tolerance for DNN models, thereby regulating the
training process and mitigating overfitting. Additionally, FedVaccine overcomes
the limitations of existing FL-based AMC models’ linear aggregation by
employing a split-learning strategy using structural clustering topology and
local queue data structure, enabling adaptive and cumulative updates to local
models. Our experimental results, including IID and non-IID datasets as well as
ablation studies, confirm FedVaccine’s robust performance and superiority over
existing FL-based AMC approaches across different noise levels. These findings
highlight FedVaccine’s potential to enhance the reliability and performance of
AMC systems in practical wireless network environments.
Source: http://arxiv.org/abs/2410.12772v1
