Securing Model Weights Against Eavesdropping Adversaries in Federated Learning Using Quantization

While security research in Federated Learning (FL) has predominantly focused on protecting client data, the confidentiality of the model parameters themselves represents a critical and underexplored vulnerability. This work addresses model reconstruction attacks by passive eavesdroppers, a threat present in common update strategies like transmitting full models or model increments. To our knowledge, we are the first to repurpose dynamic uniform quantization as a dedicated defense for model confidentiality. Our lightweight, architecture-agnostic approach combines low-bit quantization with an adaptive clipping rule to thwart reconstruction attacks, even under warm adversary initialization. We provide theoretical guarantees establishing that our defense offers persistent, non-zero protection in both protocols. Across extensive experiments on CIFAR-10 and CIFAR-100, with up to 1000 clients in heterogeneous settings, our method reduces the adversary's test accuracy to near-random levels while maintaining global accuracy within 4\% of the unquantized baseline. Our findings establish that repurposing quantization is a simple yet highly effective strategy for securing the largely overlooked area of model confidentiality in FL.