@Article{cmc.2026.086183,
AUTHOR = {Jincheng Cai, Li Feng, Ni Zhao},
TITLE = {-FedVAE: Detached Posterior-Confidence Gating for Dimension-Wise KL Regularization in Personalized Federated Collaborative Filtering},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {89},
YEAR = {2026},
NUMBER = {2},
PAGES = {--},
URL = {http://www.techscience.com/cmc/v89n2/68814},
ISSN = {1546-2226},
ABSTRACT = {Federated Variational Autoencoders (VAEs) keep interaction data local, but existing federated VAE recommenders typically apply uniform KL regularization and do not adapt dimension-wise penalties to unreliable posteriors in sparse interaction scenarios. We propose <math id="mml-ieqn-4"><mi>α</mi></math>-FedVAE, which uses a detached, clipped normalized signal-to-noise ratio as a local confidence gate for each KL dimension of a fused user posterior, without extra communication. Across MovieLens-100K, MovieLens-1M, and Amazon Video, <math id="mml-ieqn-5"><mi>α</mi></math>-FedVAE improves mean HR@20 by 7.8%–55.3% and NDCG@20 by 8.0%–65.8% over FedDAE. These results indicate that <math id="mml-ieqn-6"><mi>α</mi></math>-FedVAE improves personalized recommendation under sparse and decentralized settings while preserving the communication footprint.},
DOI = {10.32604/cmc.2026.086183}
}