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New DP-SGD method updates fewer coordinates for efficiency

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method called TP-TopK DP-SGD to improve the efficiency of differentially private stochastic gradient descent. This technique aims to reduce the computational overhead by updating fewer coordinates during private training without sacrificing optimization signal. The method involves a two-phase approach where an initial private phase identifies relevant coordinates for the main training phase, potentially reducing noise impact from the full parameter dimension to a smaller active dimension. AI

IMPACT This research could lead to more efficient and scalable differentially private machine learning models.

RANK_REASON This is a research paper detailing a new method for DP-SGD.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Huiqi Zhang, Fang Xie ·

    When Do Fewer Coordinates Suffice in DP-SGD?

    arXiv:2606.04375v1 Announce Type: cross Abstract: Differentially private stochastic gradient descent (DP-SGD) injects noise into every updated coordinate, making the injected noise energy scale with the ambient parameter dimension \(d\). We ask when private training can update fe…

  2. arXiv stat.ML TIER_1 English(EN) · Fang Xie ·

    When Do Fewer Coordinates Suffice in DP-SGD?

    Differentially private stochastic gradient descent (DP-SGD) injects noise into every updated coordinate, making the injected noise energy scale with the ambient parameter dimension \(d\). We ask when private training can update fewer coordinates without losing the signal needed f…

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