Decentralized Federated Learning (DFL), a paradigm for managing big data in a privacy-preserving and distributed manner, is vulnerable to poisoning attacks where malicious clients tamper with data or models. Current defense methods often assume Independently and Identically Distributed (IID) data across participants, which is unrealistic in real-world applications. In more realistic non-IID contexts, existing defensive strategies face challenges when distinguishing between models that have been compromised and those that have been trained on heterogeneous data distributions (non-IID), leading to diminished efficacy. In response, this paper proposes a framework that employs the Moving Target Defense (MTD) approach to bolster the robustness of DFL models. By continuously modifying the attack surface of the DFL system, the framework aims to mitigate poisoning attacks effectively. The proposed solution includes both proactive and reactive modes, utilizing a reputation system that combines metrics of model similarity and loss, alongside various defensive techniques. Comprehensive experimental evaluations indicate that the MTD-based mechanism significantly mitigates a range of poisoning attack types across multiple datasets with different federation topologies.

Synthetic data generation has emerged as a powerful solution to meet the demand for high-quality, diverse, and privacy-preserving data in many domains. Still, there is an open challenge when dealing with class imbalance and privacy preservation in synthetic tabular data generation. Thus, this study introduces two algorithms: balanced Tabular Generative Adversarial Network (b-TGAN) and balanced Tabular Principle Component Analysis (b-TPCA). While b-TGAN proactively tackles class imbalance by incorporating a re-balancing mechanism and leveraging an Autoencoder, b-TPCA offers a privacy-preserving solution by generating synthetic data using statistical properties. Through experiments on five datasets, this study demonstrates the effectiveness of b-TGAN in generating balanced data, particularly in improving the performance on minority classes. b-TPCA also shows promising results, achieving comparable ML utility to the baseline method while enhancing privacy preservation.