Presentation
Holocode: Hybrid Optical-Electronic Edge Encoding for Privacy-Preserving Cloud Training
DescriptionPrivacy-preserving machine learning aims to defend against adversaries without sacrificing task accuracy.
In latency-critical and resource-constrained settings, existing cryptographic and encoding approaches
incur heavy overheads that translate into intolerable delays and energy costs.
We present HoloCode, a hybrid optical–electronic encoding pipeline that delivers strong privacy with sub-5ms latency at a fraction of the energy of prior state-of-the-art.
HoloCode encodes only task-relevant signals while shielding sensitive features, resists inversion attacks, and locks models with a private key to prevent misuse.
HoloCode builds on an edge–cloud collaboration framework, where inference is pushed to the edge to cut latency, at the cost of higher edge energy.
To break this trade-off, we adopt an optical–digital hybrid pipeline that leverages zero-energy optical processing to reduce latency and edge energy simultaneously.
Against strong privacy-preserving baselines, HoloCode achieves10x faster inference and 50% lower edge energy, while preserving accuracy and resisting privacy feature leakage and reconstruction attacks.
In latency-critical and resource-constrained settings, existing cryptographic and encoding approaches
incur heavy overheads that translate into intolerable delays and energy costs.
We present HoloCode, a hybrid optical–electronic encoding pipeline that delivers strong privacy with sub-5ms latency at a fraction of the energy of prior state-of-the-art.
HoloCode encodes only task-relevant signals while shielding sensitive features, resists inversion attacks, and locks models with a private key to prevent misuse.
HoloCode builds on an edge–cloud collaboration framework, where inference is pushed to the edge to cut latency, at the cost of higher edge energy.
To break this trade-off, we adopt an optical–digital hybrid pipeline that leverages zero-energy optical processing to reduce latency and edge energy simultaneously.
Against strong privacy-preserving baselines, HoloCode achieves10x faster inference and 50% lower edge energy, while preserving accuracy and resisting privacy feature leakage and reconstruction attacks.
Event Type
Research Manuscript
TimeMonday, July 2711:23am - 11:36am PDT
LocationMtg Room 203C