Advances in communication technologies have contributed to the proliferation of distributed datasets. The most effective approach to distributed learning is to learn locally and then combine the local models. In general, distributed algorithms assume that there is a single model that could be induced from the distributed datasets. Under this view, distribution is treated exclusively as a technical issue. However, real-world distributed datasets frequently present an intrinsic data skewness among their partitions. Despite of its importance, up to the authors’ knowledge, its impact has been barely investigated in the literature. In this paper, the performance of different cluster-based distributed learning methods is analyzed over distinct scenarios by incrementing the differences in the probabilistic distribution of data among partitions. Based on these results the best approach is suggested at every scenario.
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
Tel.: +1 703 830 6300
Fax: +1 703 830 2300 email@example.com
(Corporate matters and books only) IOS Press c/o Accucoms US, Inc.
For North America Sales and Customer Service
West Point Commons
Lansdale PA 19446
Tel.: +1 866 855 8967
Fax: +1 215 660 5042 firstname.lastname@example.org