Fast and Secure Distributed Nonnegative Matrix Factorization

Nonnegative matrix factorization (NMF) has been successfully applied in several data mining tasks. Recently, there is an increasing interest the acceleration of NMF, due to its high cost on large matrices. On other hand, privacy issue NMF over federated worthy attention, since prevalently image and text analysis which may involve leveraging (e.g, medical record) across parties (e.g., hospitals). In this paper, we study security problems distributed NMF. Firstly, propose a sketched alternating nonnegative least squares (DSANLS) framework for utilizes sketching technique reduce size subproblems with convergence guarantee. For second problem, show that DSANLS modification can be adapted setting, but only one or limited iterations. Consequently, four efficient methods both synchronous asynchronous settings We conduct extensive experiments real datasets superiority our proposed methods. The implementation available at https://github.com/qianyuqiu79/DSANLS.