New method estimates KL divergence with differential privacy in federated learning

Researchers have developed FedPriKL, a new method for estimating Kullback–Leibler divergence in federated learning environments while ensuring differential privacy. This approach aims to address the challenges of directly sharing distribution information due to privacy concerns or communication costs. FedPriKL is designed to be unbiased with low sensitivity and variance, offering strong utility under differential privacy. Empirical studies show it achieves accuracy comparable to non-private methods and outperforms a baseline privacy-preserving variant. AI

IMPACT Enhances privacy-preserving techniques for distributed machine learning applications.

RANK_REASON The item is a research paper detailing a novel method for estimating KL divergence with differential privacy. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• FedPriKL
• Gotit.pub
• Hugging Face
• IArxiv
• Kullback–Leibler divergence
• Sayan Biswas
• ScienceCast

• paper
• safety

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