Practical Solutions and Value of DP-Norm Algorithm in Decentralized Federated Learning
Overview
Federated Learning (FL) is a solution for decentralized model training focusing on data privacy in areas like medical analysis and voice processing.
Challenges Addressed
Recent FL advancements tackle privacy challenges caused by non-IID data by integrating Differential Privacy (DP) techniques to add controlled noise, enhancing privacy.
DP-Norm Algorithm
A research team introduces DP-Norm, a primal-dual differential privacy algorithm with denoising normalization, improving robustness against non-IID data and ensuring privacy during message passing.
Key Features
– DP diffusion process in Edge Consensus Learning
– Denoising process to control norm increases
– Update rule derived using operator splitting techniques
– Incorporation of denoising normalization term to prevent noise buildup
Benefits
DP-Norm reduces gradient drift, improves model convergence, and outperforms other decentralized approaches in noise levels and convergence, especially in higher privacy settings.
Experimental Validation
Using the Fashion MNIST dataset, DP-Norm shows superior performance compared to previous approaches (DP-SGD, DP-ADMM) in image classification under various privacy settings.
Conclusion
DP-Norm is a privacy-preserving method for decentralized FL, ensuring steady performance, noise reduction, and outperforming other algorithms in both theoretical and experimental assessments.
For more details and the full research paper, visit MarkTechPost.
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