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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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.