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Towards Privacy-Preserving and Verifiable Federated Matrix Factorization
[article]
2022
arXiv
pre-print
Recent years have witnessed the rapid growth of federated learning (FL), an emerging privacy-aware machine learning paradigm that allows collaborative learning over isolated datasets distributed across multiple participants. The salient feature of FL is that the participants can keep their private datasets local and only share model updates. Very recently, some research efforts have been initiated to explore the applicability of FL for matrix factorization (MF), a prevalent method used in
arXiv:2204.01601v2
fatcat:366tl652orer7fcvhaxy7qz7yi