摘要：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), we have to deal with challenging non-convex and non-smooth problems (due to constraints or regularization) with two blocks of variables, in the presence of non-i.i.d. data. In this talk, we address the challenge by first proposing a new FedMF algorithm, namely, FedMAvg, based on the model averaging principle, for a general MF model. We further restrict to the friendly Frobenius cost function, and propose a gradient sharing based FedMF algorithm, called FedMGS, that enjoys great robustness against heterogeneous data distribution. 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 for the two algorithms are analyzed, which delineate the impacts of data distribution, local update number, and partial client communication on the algorithm performance. By focusing on a data clustering task, extensive experiment results are presented to examine the practical performance of both algorithms, as well as demonstrating their efficacy over the existing distributed clustering algorithms.

Biography：张纵辉教授分别于2003年与2008年自台湾清华大学取得电机工程学士与通讯工程博士学位。目前他任职于香港中文大学(深圳)理工学院与深圳市大数据研究院担任副教授。在2012年八月到2015年七月期间，他在台湾科技大学电子工程系担任助理教授。在担任教职以前，张教授先后在台湾清华大学(2008-2011)与美国加州大学戴维斯分校(2011-2012)担任博士后研究员。他曾是美国密尼苏达大学双城分校、香港中文大学、美国南加州大学的访问学者。

张教授的研究兴趣主要于通信系统与机器学习中的关键信号处理与优化方法。他在2014年获得台湾科技大学年轻学者研究奖，2015年获得IEEE通信学会亚太区杰出年轻学者奖, 2018年获得IEEE信号处理学会最佳论文奖。他曾担任IEEE TRANSACTIONS ON SIGNAL PROCESSING 与 IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS的编委(Associate editor)。目前，张教授是IEEE OPEN JOURNAL OF SIGNAL PROCESSING的编委，他也是IEEE信号处理学会通信与网络信号处理技术委员会(IEEE SPS SPCOM TC)的成员。