Hyperdimensional Computing for Secure Federated Learning

Abstract

Federated learning (FL) presents a promising paradigm for training machine learning models across decentralized devices while preserving data privacy. However, the inherent vulnerability of FL to adversarial attacks poses a significant threat to its security and reliability. This paper proposes a novel approach to secure FL by leveraging hyperdimensional computing (HDC). HDC utilizes high-dimensional vectors with near-binary values, offering inherent robustness against noise and providing a strong foundation for secure aggregation and communication. The core claim is that HDC's properties provide a robust defense against gradient manipulation, a common attack vector in FL. The proposed mechanism utilizes HDC's capacity for complex pattern recognition to create a resilient aggregation process. This approach represents a fundamentally new strategy for securing FL, diverging from conventional cryptographic solutions. We outline the key components of the HDC-based FL system, focusing on the design of the HDC vectors, the aggregation protocol, and the validation process. The system is designed to minimize the impact of adversarial gradients while maintaining model accuracy. We demonstrate the potential of HDC to significantly enhance the security and robustness of FL systems.