Practical Lossless Federated Singular Vector Decomposition over Billion-Scale Data. (arXiv:2105.08925v3 [cs.DC] UPDATED)

With the enactment of privacy-preserving regulations, e.g., GDPR, federated
SVD is proposed to enable SVD-based applications over different data sources
without revealing the original data. However, many SVD-based applications
cannot be well supported by existing federated SVD solutions. The crux is that
these solutions, adopting either differential privacy (DP) or homomorphic
encryption (HE), suffer from accuracy loss caused by unremovable noise or
degraded efficiency due to inflated data.

In this paper, we propose FedSVD, a practical lossless federated SVD method …

data dc scale