Vertical Federated Learning (VFL) allows multiple entities with feature-partitioned datasets to collaboratively train machine learning models while keeping their data private. However, many existing VFL approaches either rely on a single trusted server or incur significant computational and communication overhead due to encryption-based techniques. In this paper, we propose FedMod, a scalable and lightweight VFL framework that removes the need for trusted parties or cryptographic primitives. FedMod introduces a novel multi-server architecture combined with additive secret sharing to protect intermediate computations during training. We conduct extensive experiments across multiple real-world datasets and benchmark FedMod against state-of-the-art methods including homomorphic encryption, differential privacy, and functional encryption. Our results show that FedMod achieves comparable or superior accuracy with significantly lower computation time and communication cost. Moreover, FedMod provides strong protection in the semi-honest setting and remains secure even when some parties or servers partially collude. These results highlight FedMod’s practicality for real-world privacy-preserving collaborative learning scenarios.