System-level evaluation reveals that network constraints and hardware costs, rather than raw latency, often dictate the optimal choice between MPC and FHE for privacy-preserving ML.
Accelerating ReLU for MPC-based private inference with a communication-efficient sign estimation
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Beyond Latency: A System-Level Characterization of MPC and FHE for PPML
System-level evaluation reveals that network constraints and hardware costs, rather than raw latency, often dictate the optimal choice between MPC and FHE for privacy-preserving ML.