Sparsity-Based Secure Gradient Aggregation for Resource-Constrained Federated Learning

Secure aggregation is an information-theoretic mechanism for gradient aggregation in federated learning, to aggregate the local user gradients without revealing them in the clear. In this work, we study secure aggregation under gradient sparsification constraints, for resource-limited wireless networks, where only a small fraction of local parameters are aggregated from each user during training (as opposed to the full gradient). We demonstrate that conventional mechanisms can reveal sensitive user data when aggregating sparsified gradients, due to the auxiliary coordinate information shared during sparsification, even when the individual gradients are not disclosed in the clear. We then propose a coordinate-hiding sparsified secure aggregation mechanism to address this challenge, which hides both the gradient parameters and the associated coordinates under formal information-theoretic privacy guarantees. Our framework reduces the communication overhead of conventional secure aggregation baselines by an order of magnitude without compromising model accuracy.