Distributed clustering in partially overlapping feature spaces
This addresses a novel distributed clustering scenario relevant for applications like healthcare, where institutions have complementary data, but it is incremental as it builds on existing clustering and federated learning methods.
The paper tackles the problem of distributed clustering when participants have private datasets with overlapping subsets of features, proposing two algorithms: a federated iterative method and a one-shot statistical merging approach. It shows that under certain conditions, these algorithms converge to the optimal centralized solution, with practical tests conducted on three public datasets.
We introduce and address a novel distributed clustering problem where each participant has a private dataset containing only a subset of all available features, and some features are included in multiple datasets. This scenario occurs in many real-world applications, such as in healthcare, where different institutions have complementary data on similar patients. We propose two different algorithms suitable for solving distributed clustering problems that exhibit this type of feature space heterogeneity. The first is a federated algorithm in which participants collaboratively update a set of global centroids. The second is a one-shot algorithm in which participants share a statistical parametrization of their local clusters with the central server, who generates and merges synthetic proxy datasets. In both cases, participants perform local clustering using algorithms of their choice, which provides flexibility and personalized computational costs. Pretending that local datasets result from splitting and masking an initial centralized dataset, we identify some conditions under which the proposed algorithms are expected to converge to the optimal centralized solution. Finally, we test the practical performance of the algorithms on three public datasets.