Federated Matrix Factorization: Algorithm Design and Application to Data Clustering

Understand

Recent demands on data privacy have called for federated learning (FL) as a new distributed learning paradigm in massive and heterogeneous networks.

• Although many FL algorithms have been proposed, few of them have considered the matrix factorization (MF) model, which is known to have a vast number of signal processing and machine learning applications.
• Different from the existing FL algorithms that are designed for smooth problems with single block of variables, in federated MF (FedMF), one has to deal with challenging non-convex and non-smooth problems (due to constraints or regularization) with two blocks of variables.
• In this paper, we address the challenge by proposing two new FedMF algorithms, namely, FedMAvg and FedMGS, based on the model averaging and gradient sharing principles, respectively.