Lightweight and Scalable Secure Aggregation for Federated Learning

The explosive growth of distributed mobile devices has made federated learning (FL) a key privacy-preserving approach for collaborative training without sharing sensitive data. However, its untrusted decentralized nature exposes it to threats from malicious servers that can disrupt training or compromise the global model. Although secure aggregation methods are developed to mitigate these risks, many existing solutions rely on computationally heavy cryptographic techniques, introducing significant overhead. To overcome this limitation, we propose LiteSecFL, a lightweight and scalable secure aggregation framework for federated learning that tackles threats from malicious aggregators. The core of our approach is PolyAgg, a secure aggregation protocol that innovatively uses polynomial commitment with additive homomorphism to effectively defend against malicious aggregators. Furthermore, to reduce verification overhead, we incorporate a random sampling technique into our framework. Security analysis and experimental results across diverse models with different sizes, and various levels of sampling demonstrate the efficacy of LiteSecFL. The findings reveal that our LiteSecFL not only outperforms state-of-the-art methods by up to 9x in speed but is also scalable to different sizes of model architectures and the number of clients.