A naukri.com initiative
Arxiv
3d
390
Image Credit: Arxiv
Share Secrets for Privacy: Confidential Forecasting with Vertical Federated Learning
- Vertical federated learning (VFL) is utilized for time series forecasting in areas like healthcare and manufacturing.
- Challenges include data privacy and overfitting on small, noisy datasets.
- A novel framework called "Secret-shared Time Series Forecasting with VFL" (STV) is proposed to address these challenges.
- STV features privacy-preserving algorithms for forecasting with SARIMAX and autoregressive trees on vertically-partitioned data.
- Decentralized forecasting is achieved through secret sharing and multi-party computation in STV.
- N-party algorithms for matrix multiplication and inverse operations aid in parameter optimization, ensuring strong convergence with minimal tuning complexity.
- STV's performance is evaluated on six datasets from various contexts, showing comparable forecasting accuracy to centralized approaches.
- STV's exact optimization surpasses centralized methods by 23.81% in forecasting accuracy, including state-of-the-art models like long short-term memory.
- The scalability of STV is assessed by comparing communication costs of exact and iterative optimization.
- The code and supplementary material for STV are accessible online at https://github.com/adis98/STV.
Read Full Article
23 Likes
For uninterrupted reading, download the app