FaaS and Furious: Accelerating Privacy-Preserving ML with Function as a Service at the Edge

The demand for Privacy-Preserving Machine Learning (PPML) is growing, facing challenges in privacy balance, computational efficiency, and real-world feasibility, as traditional cloud approaches often suffer from high latency and resource limitations. Our paper introduces an innovative approach leveraging Function as a Service (FaaS) and edge computing to address these issues, significantly accelerating encrypted ML inference with strong privacy guarantees. Using Hybrid Homomorphic Encryption (HHE) and a distributed serverless architecture, we build a scalable solution that limits computational overhead and maximises resource utilisation. Offloading compute-intensive ML inference tasks to stateless functions, allocated on-demand at the edge, enables parallel processing, minimising latency and improving execution time. Evaluations on real-world medical datasets show substantial improvements over conventional methods, demonstrating feasible low-latency, high-efficiency PPML in distributed environments. Our findings highlight the potential of edge-driven FaaS architectures to bridge security and speed, paving the way for practical, real-time, privacy-preserving AI.