  The rapid proliferation of the Internet of Things (IoT) has ushered in
transformative connectivity between physical devices and the digital realm.
Nonetheless, the escalating threat of Distributed Denial of Service (DDoS)
attacks jeopardizes the integrity and reliability of IoT networks. Conventional
DDoS mitigation approaches are ill-equipped to handle the intricacies of IoT
ecosystems, potentially compromising data privacy. This paper introduces an
innovative strategy to bolster the security of IoT networks against DDoS
attacks by harnessing the power of Federated Learning that allows multiple IoT
devices or edge nodes to collaboratively build a global model while preserving
data privacy and minimizing communication overhead. The research aims to
investigate Federated Learning's effectiveness in detecting and mitigating DDoS
attacks in IoT. Our proposed framework leverages IoT devices' collective
intelligence for real-time attack detection without compromising sensitive
data. This study proposes innovative deep autoencoder approaches for data
dimensionality reduction, retraining, and partial selection to enhance the
performance and stability of the proposed model. Additionally, two renowned
aggregation algorithms, FedAvg and FedAvgM, are employed in this research.
Various metrics, including true positive rate, false positive rate, and
F1-score, are employed to evaluate the model. The dataset utilized in this
research, N-BaIoT, exhibits non-IID data distribution, where data categories
are distributed quite differently. The negative impact of these distribution
disparities is managed by employing retraining and partial selection
techniques, enhancing the final model's stability. Furthermore, evaluation
results demonstrate that the FedAvgM aggregation algorithm outperforms FedAvg,
indicating that in non-IID datasets, FedAvgM provides better stability and
performance.
