Secured-FL: Blockchain-Based Defense against Adversarial Attacks on Federated Learning Models

Federated Learning (FL) enables joint training over distributed devices without data exchange but is highly vulnerable to attacks by adversaries in the form of model poisoning and malicious update injection. This work proposes Secured-FL, a blockchain-based defensive framework that combines smart contract–based authentication, clustering-driven outlier elimination, and dynamic threshold adjustment to defend against adversarial attacks. The framework was implemented on a private Ethereum network with a Proof-of-Authority consensus algorithm to ensure tamper-resistant and auditable model updates. Large-scale simulation on the Cyber Data dataset, under up to 50% malicious client settings, demonstrates Secured-FL achieves 6%–12% higher accuracy, 9%–15% lower latency, and approximately 14% less computational expense compared to the PPSS benchmark framework. Additional tests, including confusion matrices, ROC and Precision-Recall curves, and ablation tests, confirm the interpretability and robustness of the defense. Tests for scalability also show consistent performance up to 500 clients, affirming appropriateness to reasonably large deployments. These results make Secured-FL a feasible, adversarially resilient FL paradigm with promising potential for application in smart cities, medicine, and other mission-critical IoT deployments.