Secure Floating-Point Training

Secure 2-party computation (2PC) of floating-point arithmetic is improving in performance and recent work runs deep learning algorithms with it, while being as numerically precise as commonly used machine learning (ML) frameworks like PyTorch. We find that the existing 2PC libraries for floating-point support generic computations and lack specialized support for ML training. Hence, their latency and communication costs for compound operations (e.g., dot products) are high. We provide novel specialized 2PC protocols for compound operations and prove their precision using numerical analysis. Our implementation B EACON outperforms state-of-the-art libraries for 2PC of floating-point by over 6 × .