  Non-negative matrix factorization (NMF) based topic modeling is widely used
in natural language processing (NLP) to uncover hidden topics of short text
documents. Usually, training a high-quality topic model requires large amount
of textual data. In many real-world scenarios, customer textual data should be
private and sensitive, precluding uploading to data centers. This paper
proposes a Federated NMF (FedNMF) framework, which allows multiple clients to
collaboratively train a high-quality NMF based topic model with locally stored
data. However, standard federated learning will significantly undermine the
performance of topic models in downstream tasks (e.g., text classification)
when the data distribution over clients is heterogeneous. To alleviate this
issue, we further propose FedNMF+MI, which simultaneously maximizes the mutual
information (MI) between the count features of local texts and their topic
weight vectors to mitigate the performance degradation. Experimental results
show that our FedNMF+MI methods outperform Federated Latent Dirichlet
Allocation (FedLDA) and the FedNMF without MI methods for short texts by a
significant margin on both coherence score and classification F1 score.
