FedRAN achieves up to 4.8 pp higher accuracy in federated continual learning while using 30-122× less per-client communication by transmitting truncated-SVD summaries of random-feature Gram matrices and performing closed-form ridge classification after two-level QR-SVD merging.
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Presents a unified incremental SVD framework with a projection-based rule for rank-1 updates and systematic comparisons of periodic, error-threshold, angle-threshold, and adaptive refresh policies, claiming near full-SVD accuracy at lower cost on synthetic and ETF data.
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Accurate and Resource-Efficient Federated Continual Learning
FedRAN achieves up to 4.8 pp higher accuracy in federated continual learning while using 30-122× less per-client communication by transmitting truncated-SVD summaries of random-feature Gram matrices and performing closed-form ridge classification after two-level QR-SVD merging.
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Incremental SVD for Large-Scale Dynamic Matrices: Accuracy, Subspace Stability, Refresh Strategies, and Financial Factor-Based Risk Models
Presents a unified incremental SVD framework with a projection-based rule for rank-1 updates and systematic comparisons of periodic, error-threshold, angle-threshold, and adaptive refresh policies, claiming near full-SVD accuracy at lower cost on synthetic and ETF data.