Continuous Federation of Noise-resistant Heterogeneous Medical Dialogue Using the Trustworthiness-based Evaluation

LIU Yupeng; ZHANG Jiang; TANG Shichen; MENG Xin; MENG Qingfeng

doi:10.11999/JEIT250057

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LIU Yupeng, ZHANG Jiang, TANG Shichen, MENG Xin, MENG Qingfeng. Continuous Federation of Noise-resistant Heterogeneous Medical Dialogue Using the Trustworthiness-based Evaluation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250057

Citation:

LIU Yupeng, ZHANG Jiang, TANG Shichen, MENG Xin, MENG Qingfeng. Continuous Federation of Noise-resistant Heterogeneous Medical Dialogue Using the Trustworthiness-based Evaluation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250057

Citation:

LIU Yupeng, ZHANG Jiang, TANG Shichen, MENG Xin, MENG Qingfeng. Continuous Federation of Noise-resistant Heterogeneous Medical Dialogue Using the Trustworthiness-based Evaluation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250057

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Continuous Federation of Noise-resistant Heterogeneous Medical Dialogue Using the Trustworthiness-based Evaluation

doi: 10.11999/JEIT250057 cstr: 32379.14.JEIT250057

School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150006, China

Funds: The National Natural Science Foundation of China (61300115), China Postdoctoral Science Foundation (2014M561331), The Scientific and Technological Research Project of Heilongjiang Provincial Department of Education (12521073)

Received Date: 2025-01-22
Rev Recd Date: 2025-07-13

Available Online: 2025-07-22

Abstract

Abstract

Objective To address the key challenges of client model heterogeneity, data distribution heterogeneity, and text noise in medical dialogue federated learning, this paper proposes a trustworthiness-based, noise-resistant heterogeneous medical dialogue federated learning method, termed FedRH. FedRH enhances robustness by improving the objective function, aggregation strategy, and local update process, among other components, based on credibility evaluation. Methods Model training is divided into a local training stage and a heterogeneous federated learning stage. During local training, text noise is mitigated using a symmetric cross-entropy loss function, which reduces the risk of overfitting to noisy text. In the heterogeneous federated learning stage, an adaptive aggregation mechanism incorporates clean, noisy, and heterogeneous client texts by evaluating their quality. Local parameter updates consider both local and global parameters simultaneously, enabling continuous adaptive updates that improve resistance to both random and structured (syntax/semantic) noise and model heterogeneity. The main contributions are threefold: (1) A local noise-resistant training strategy that uses symmetric cross-entropy loss to prevent overfitting to noisy text during local training; (2) A heterogeneous federated learning approach based on client trustworthiness, which evaluates each client’s text quality and learning effectiveness to compute trust scores. These scores are used to adaptively weight clients during model aggregation, thereby reducing the influence of low-quality data while accounting for text heterogeneity; (3) A local continuous adaptive aggregation mechanism, which allows the local model to integrate fine-grained global model information. This approach reduces the adverse effects of global model bias caused by heterogeneous and noisy text on local updates. Results and Discussions The effectiveness of the proposed model is systematically validated through extensive, multi-dimensional experiments. The results indicate that FedRH achieves substantial improvements over existing methods in noisy and heterogeneous federated learning scenarios (Table 2, Table 3). The study also presents training process curves for both heterogeneous models (Figure 3) and isomorphic models (Figure 6), supplemented by parameter sensitivity analysis, ablation experiments, and a case study. Conclusions The proposed FedRH framework significantly enhances the robustness of federated learning for medical dialogue tasks in the presence of heterogeneous and noisy text. The main conclusions are as follows: (1) Compared to baseline methods, FedRH achieves superior performance in client-side models under heterogeneous and noisy text conditions. It demonstrates improvements across multiple metrics, including precision, recall, and factual consistency, and converges more rapidly during training. (2) Ablation experiments confirm that both the symmetric cross-entropy-based local training strategy and the credibility-weighted heterogeneous aggregation approach contribute to performance gains.
- Trustworthiness,
- Medical conversation,
- Noise text,
- Heterogeneous text,
- Continuous adaptation learning

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References(34)

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