Federated Learning (FL) endorses promising avenues for organizations, enabling collaborative model training across a distributed client network, exempting clients from relaying raw data, thus preserving privacy and lowering communication overhead. Despite these benefits, the decentralized nature of FL systems remains highly susceptible to adversarial threats, particularly Poisoning Attacks (PA) from the network’s edge. Countermeasures against PA are often tailored to specific client behaviors, yet the global model’s integrity remains at risk due to evolving malicious strategies. The author aims to bridge this gap by proposing a feature representation learner using a novel unsupervised detection framework. It leverages an Ensemble of Deep Generative Models (EDGMs) to assign suspicious scores to model updates, eliminating irregularities and aggregating only legitimate contributions. The objective is to deflect anomalies through continuous representation learning and provide a practical solution grounded in theoretical models. The system underwent preliminary testing under limited experimental configurations, in line with available resources. Results showed promising detection accuracy—around 90% on MNIST and 70–80% on CIFAR-10—while mitigating poisoning even when 20–40% of clients were compromised. Future work includes improving client data diversity, boosting detection efficiency, and enhancing system scalability.