Distributed Gradient Clustering: Convergence and the Effect of Initialization
We study the effects of center initialization on the performance of a family of distributed gradient-based clustering algorithms introduced in [1], that work over connected networks of users. In the considered scenario, each user contains a local dataset and communicates only with its immediate neighbours, with the aim of finding a global clustering of the joint data. We perform extensive numerical experiments, evaluating the effects of center initialization on the performance of our family of methods, demonstrating that our methods are more resilient to the effects of initialization, compared to centralized gradient clustering [2]. Next, inspired by the $K$-means++ initialization [3], we propose a novel distributed center initialization scheme, which is shown to improve the performance of our methods, compared to the baseline random initialization.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
An Efficient Framework for Clustered Federated Learning
We address the problem of federated learning (FL) where users are distributed and partitioned into clusters. This setup captures settings where different groups of users have their own objectives (learning tasks) but by …
ClusteringFederated LearningMulti-Task LearningProbabilistic K-means Clustering via Nonlinear Programming
K-means is a classical clustering algorithm with wide applications. However, soft K-means, or fuzzy c-means at m=1, remains unsolved since 1981. To address this challenging open problem, we propose a novel clustering mod…
ClusteringDifferentially Private Federated $k$-Means Clustering with Server-Side Data
Clustering is a cornerstone of data analysis that is particularly suited to identifying coherent subgroups or substructures in unlabeled data, as are generated continuously in large amounts these days. However, in many c…
ClusteringOn Distributed Non-convex Optimization: Projected Subgradient Method For Weakly Convex Problems in Networks
The stochastic subgradient method is a widely-used algorithm for solving large-scale optimization problems arising in machine learning. Often these problems are neither smooth nor convex. Recently, Davis et al. [1-2] cha…
Compressive SensingDictionary LearningRetrievalGradient Based Clustering
We propose a general approach for distance based clustering, using the gradient of the cost function that measures clustering quality with respect to cluster assignments and cluster center positions. The approach is an i…
Clustering