Federated Learning (FL) enables multiple users to collaboratively train a global model in a distributed manner without revealing their personal data. However, FL remains vulnerable to model poisoning attacks, where malicious actors inject crafted updates to compromise the global model's accuracy. These vulnerabilities are particularly severe in non-homogeneous environments, where clients exhibit varying proportions of class labels, resulting in heterogeneous updates. In such settings, benign outliers are often misclassified as false positives, while maliciously crafted uploads evade detection and are aggregated at the server. Existing defense mechanisms struggle in such real-world settings, resulting in significant declines in the global FL model's performance. We propose a novel defense mechanism, Kernel-based Trust Segmentation (KeTS), to counter model poisoning attacks. Unlike existing approaches, KeTS analyzes the evolution of each client's updates and effectively segments malicious clients using Kernel Density Estimation (KDE), even in the presence of benign outliers. We thoroughly evaluate KeTS's performance against the six most effective model poisoning attacks (i.e., Trim-Attack, Krum-Attack, Min-Max attack, Min-Sum attack, and their variants) on two different datasets (i.e., MNIST and Fashion-MNIST) and compare its performance with three classical robust schemes (i.e., Krum, Trim-Mean, and Median) and a state-of-the-art defense (i.e., FLTrust). Our results show that KeTS outperforms the existing defenses in every attack setting; beating the best-performing defense by an overall average of >24% (on MNIST) and >14% (on Fashion-MNIST). A series of further experiments (varying poisoning approaches, attacker population, etc.) reveal the consistent and superior performance of KeTS under diverse conditions.

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