# Federated Learning for Short Text Clustering

arXiv: https://arxiv.org/abs/2312.07556 · HTML: https://mlsanity.com/paper/2312.07556/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 36 references that are in the map.
- First posted on arXiv 23 Nov 2023.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Self-labelling via simultaneous clustering and representation learning](https://mlsanity.com/paper/1911.05371/) (2019)
- [An Efficient Framework for Clustered Federated Learning](https://mlsanity.com/paper/2006.04088/) (2020)
- [Robust Loss Functions under Label Noise for Deep Neural Networks](https://mlsanity.com/paper/1712.09482/) (2017)
- [FedProto: Federated Prototype Learning across Heterogeneous Clients](https://mlsanity.com/paper/2105.00243/) (2021)
- [Supporting Clustering with Contrastive Learning](https://mlsanity.com/paper/2103.12953/) (2021)
- [Self-Taught Convolutional Neural Networks for Short Text Clustering](https://mlsanity.com/paper/1701.00185/) (2017)
- [Towards Federated Clustering: A Federated Fuzzy c-Means Algorithm (FFCM)](https://mlsanity.com/paper/2201.07316/) (2022)
- [DeepGUM: Learning Deep Robust Regression with a Gaussian-Uniform Mixture Model](https://mlsanity.com/paper/1808.09211/) (2018)
- [Robust improper maximum likelihood: tuning, computation, and a comparison with other methods for robust Gaussian clustering](https://mlsanity.com/paper/1406.0808/) (2014)

## Abstract (arXiv)

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.