Federated Learning for Short Text Clustering
Mengling Hu Chaochao Chen Weiming Liu Xinting Liao Xiaolin Zheng
Short text clustering has been popularly studied for its significance in
mining valuable insights from many short texts. In this paper, we focus on the
federated short text clustering (FSTC) problem, i.e., clustering short texts
that are distributed in different clients, which is a realistic problem under
privacy requirements. Compared with the centralized short text clustering
problem that short texts are stored on a central server, the FSTC problem has
not been explored yet. To fill this gap, we propose a Federated Robust Short
Text Clustering (FSTC) framework. FSTC includes two main modules, i.e., robust
short text clustering module and federated cluster center aggregation module.
The robust short text clustering module aims to train an effective short text
clustering model with local data in each client. We innovatively combine
optimal transport to generate pseudo-labels with Gaussian-uniform mixture model
to ensure the reliability of the pseudo-supervised data. The federated cluster
center aggregation module aims to exchange knowledge across clients without
sharing local raw data in an efficient way. The server aggregates the local
cluster centers from different clients and then sends the global centers back
to all clients in each communication round. Our empirical studies on three
short text clustering datasets demonstrate that FSTC significantly outperforms
the federated short text clustering baselines.