Hi-SAFE: Hierarchical Secure Aggregation for Lightweight Federated Learning
This addresses privacy and efficiency issues in federated learning for IoT and edge networks, but it is incremental as it builds on existing sign-based methods like SIGNSGD-MV.
The paper tackled the challenge of ensuring privacy and communication efficiency in federated learning for resource-constrained environments like IoT and edge networks, by proposing Hi-SAFE, a lightweight and cryptographically secure aggregation framework for sign-based methods, which achieved secure evaluation with constant multiplicative depth and bounded per-user complexity independent of the number of users.
Federated learning (FL) faces challenges in ensuring both privacy and communication efficiency, particularly in resource-constrained environments such as Internet of Things (IoT) and edge networks. While sign-based methods, such as sign stochastic gradient descent with majority voting (SIGNSGD-MV), offer substantial bandwidth savings, they remain vulnerable to inference attacks due to exposure of gradient signs. Existing secure aggregation techniques are either incompatible with sign-based methods or incur prohibitive overhead. To address these limitations, we propose Hi-SAFE, a lightweight and cryptographically secure aggregation framework for sign-based FL. Our core contribution is the construction of efficient majority vote polynomials for SIGNSGD-MV, derived from Fermat's Little Theorem. This formulation represents the majority vote as a low-degree polynomial over a finite field, enabling secure evaluation that hides intermediate values and reveals only the final result. We further introduce a hierarchical subgrouping strategy that ensures constant multiplicative depth and bounded per-user complexity, independent of the number of users n.