Introduces modulated learning for private distributed regression allowing one sample per client via calibrated noise injection on samples and aggregation of transformed representations to achieve unbiased gradients in expectation.
International colloquium on automata, languages, and programming , pages=
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POOL is a new RL algorithm that adds privacy protection in continuous spaces with one-sided feedback and achieves sample complexity matching known non-private lower bounds.
Post-processing via random selection or linear combination of differentially private models allows meeting arbitrary target privacy parameters without additional training.
FedProxy replaces weak adapters with a proxy SLM for federated LLM fine-tuning, outperforming prior methods and approaching centralized performance via compression, heterogeneity-aware aggregation, and training-free fusion.
FedAvg DeepSurv across Lifelines (n=148k, self-report) and Rotterdam Study (n=10k, linked outcomes) raised C-statistics from 0.728 to 0.739 and 0.783 to 0.787 versus local training.
Systematic experiments show that text decomposition methods and privacy budget allocation strategies produce significantly different privacy-utility trade-offs even under comparable total epsilon budgets.
citing papers explorer
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Modulated learning for private and distributed regression with just a single sample per client device
Introduces modulated learning for private distributed regression allowing one sample per client via calibrated noise injection on samples and aggregation of transformed representations to achieve unbiased gradients in expectation.
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Privacy Preserving Reinforcement Learning with One-Sided Feedback
POOL is a new RL algorithm that adds privacy protection in continuous spaces with one-sided feedback and achieves sample complexity matching known non-private lower bounds.
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Differentially Private Model Merging
Post-processing via random selection or linear combination of differentially private models allows meeting arbitrary target privacy parameters without additional training.
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FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion
FedProxy replaces weak adapters with a proxy SLM for federated LLM fine-tuning, outperforming prior methods and approaching centralized performance via compression, heterogeneity-aware aggregation, and training-free fusion.
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Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction
FedAvg DeepSurv across Lifelines (n=148k, self-report) and Rotterdam Study (n=10k, linked outcomes) raised C-statistics from 0.728 to 0.739 and 0.783 to 0.787 versus local training.
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A Systematic Exploration of Text Decomposition and Budget Distribution in Differentially Private Text Obfuscation
Systematic experiments show that text decomposition methods and privacy budget allocation strategies produce significantly different privacy-utility trade-offs even under comparable total epsilon budgets.