Collaborative Chinese Text Recognition with Personalized Federated Learning

• URL: http://arxiv.org/abs/2305.05602v2
• Date: Thu, 31 Aug 2023 05:08:45 GMT
• Title: Collaborative Chinese Text Recognition with Personalized Federated Learning
• Authors: Shangchao Su, Haiyang Yu, Bin Li, Xiangyang Xue
• Abstract summary: In Chinese text recognition, it is often necessary for one organization to collect a large amount of data from similar organizations. Due to the natural presence of private information in text data, such as addresses and phone numbers, different organizations are unwilling to share private data. We introduce personalized federated learning (pFL) into the Chinese text recognition task and propose the pFedCR algorithm.
• Score: 61.34060587461462
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In Chinese text recognition, to compensate for the insufficient local data and improve the performance of local few-shot character recognition, it is often necessary for one organization to collect a large amount of data from similar organizations. However, due to the natural presence of private information in text data, such as addresses and phone numbers, different organizations are unwilling to share private data. Therefore, it becomes increasingly important to design a privacy-preserving collaborative training framework for the Chinese text recognition task. In this paper, we introduce personalized federated learning (pFL) into the Chinese text recognition task and propose the pFedCR algorithm, which significantly improves the model performance of each client (organization) without sharing private data. Specifically, pFedCR comprises two stages: multiple rounds of global model training stage and the the local personalization stage. During stage 1, an attention mechanism is incorporated into the CRNN model to adapt to various client data distributions. Leveraging inherent character data characteristics, a balanced dataset is created on the server to mitigate character imbalance. In the personalization phase, the global model is fine-tuned for one epoch to create a local model. Parameter averaging between local and global models combines personalized and global feature extraction capabilities. Finally, we fine-tune only the attention layers to enhance its focus on local personalized features. The experimental results on three real-world industrial scenario datasets show that the pFedCR algorithm can improve the performance of local personalized models by about 20\% while also improving their generalization performance on other client data domains. Compared to other state-of-the-art personalized federated learning methods, pFedCR improves performance by 6\% $\sim$ 8\%.

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