Non-independent and identically distributed (non-IID) data is a key challenge
in federated learning (FL), which usually hampers the optimization convergence
and the performance of FL. Existing data augmentation methods based on
federated generative models or raw data sharing strategies for solving the
non-IID problem still suffer from low performance, privacy protection concerns,
and high communication overhead in decentralized tabular data. To tackle these
challenges, we propose a federated tabular data augmentation method, named
Fed-TDA. The core idea of Fed-TDA is to synthesize tabular data for data
augmentation using some simple statistics (e.g., distributions of each column
and global covariance). Specifically, we propose the multimodal distribution
transformation and inverse cumulative distribution mapping respectively
synthesize continuous and discrete columns in tabular data from a noise
according to the pre-learned statistics. Furthermore, we theoretically analyze
that our Fed-TDA not only preserves data privacy but also maintains the
distribution of the original data and the correlation between columns. Through
extensive experiments on five real-world tabular datasets, we demonstrate the
superiority of Fed-TDA over the state-of-the-art in test performance and
communication efficiency.

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