Federated Matrix Factorization: Algorithm Design and Application to Data Clustering

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 o...

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Vydané v:IEEE transactions on signal processing Ročník 70; s. 1625 - 1640
Hlavní autori: Wang, Shuai, Chang, Tsung-Hui
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Clustering
Clustering algorithms
Communication
Convergence
Cost analysis
Data models
Distributed databases
Factorization
Federated learning
gradient sharing
Machine learning
matrix factorization
model averaging
Partitioning algorithms
Servers
Signal processing
Signal processing algorithms
ISSN:1053-587X, 1941-0476
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Shrnutí: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. Since the MF problem involves two blocks of variables and the variables are usually subject to constraints related to specific solution structure, it requires new FL algorithm designs to achieve communication-efficient MF in heterogeneous data networks. In this paper, we address the challenge by proposing two new federated MF (FedMF) algorithms, namely, FedMAvg and FedMGS, based on the model averaging and gradient sharing principles, respectively. Both FedMAvg and FedMGS adopt multiple steps of local updates per communication round to speed up convergence, and allow only a randomly sampled subset of clients to communicate with the server for reducing the communication cost. Convergence properties for the two algorithms are thoroughly analyzed, which delineate the impacts of heterogeneous data distribution, local update number, and partial client communication on the algorithm performance, and guide the design of proposed algorithms. By focusing on a data clustering task, extensive experiment results are presented to examine the practical performance of proposed algorithms, as well as demonstrating their efficacy over the existing distributed clustering algorithms.
Bibliografia:ObjectType-Article-1
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2022.3151505