arXiv:2605.20450v1 Announce Type: new Abstract: Differentially private stochastic gradient descent (DP-SGD) enables private deep learning through per-example clipping and calibrated Gaussian noise, but its high-variance updates can reduce utility on challenging datasets. We propose \textbf{SMA-DP-SGD}, a \textbf{Spectral Memory-Aware Differentially Private Stochastic Gradient Descent} method that augments DP-SGD with a fractional memory branch built only from previously privatized noisy releases. WeightWatcher-inspired power-law spectral exponents provide group-wise reliability signals, instan
The continuous drive for more robust and secure AI systems meets the increasing demand for data privacy, pushing research into methods like differential privacy.
Improved differentially private deep learning techniques can enhance trustworthiness and regulatory compliance for AI applications, broadening their societal and commercial adoption.
The development of SMA-DP-SGD offers a significant advancement in balancing privacy with utility in AI, potentially mitigating the performance degradation typically associated with privacy-preserving methods.
- · AI developers
- · Healthcare sector
- · Financial institutions
- · Data privacy advocates
- · Companies with weak privacy practices
- · Models relying on non-private data
More widespread adoption of privacy-preserving AI in sensitive domains.
Increased consumer trust in AI systems handling personal data.
Regulatory bodies may update privacy standards to incorporate or incentivize the use of advanced techniques like SMA-DP-SGD.
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Read at arXiv cs.LG