  Automatically mining sentiment tendency contained in natural language is a
fundamental research to some artificial intelligent applications, where
solutions alternate with challenges. Transfer learning and multi-task learning
techniques have been leveraged to mitigate the supervision sparsity and
collaborate multiple heterogeneous domains correspondingly. Recent years, the
sensitive nature of users' private data raises another challenge for sentiment
classification, i.e., data privacy protection. In this paper, we resort to
federated learning for multiple domain sentiment classification under the
constraint that the corpora must be stored on decentralized devices. In view of
the heterogeneous semantics across multiple parties and the peculiarities of
word embedding, we pertinently provide corresponding solutions. First, we
propose a Knowledge Transfer Enhanced Private-Shared (KTEPS) framework for
better model aggregation and personalization in federated sentiment
classification. Second, we propose KTEPS$^\star$ with the consideration of the
rich semantic and huge embedding size properties of word vectors, utilizing
Projection-based Dimension Reduction (PDR) methods for privacy protection and
efficient transmission simultaneously. We propose two federated sentiment
classification scenes based on public benchmarks, and verify the superiorities
of our proposed methods with abundant experimental investigations.
