With the rapid development of artificial intelligence, dialogue systems have become a prominent form of human-computer interaction. However, traditional centralized or fully local training approaches face challenges in balancing privacy preservation and personalization due to data privacy concerns and heterogeneous device capabilities. Federated learning, as a representative distributed paradigm, offers a promising solution. However, existing methods often suffer from overfitting under limited client data and tend to forget global information after multiple training rounds, leading to poor generalization. To address these issues, we propose FedDTRE, a Federated adaptive aggregation strategy for Dialogue generation based on Trustworthiness Evaluation. Instead of directly replacing local models with the global model, FedDTRE leverages trustworthiness scores of both global and local models on a fairness-oriented evaluation dataset to dynamically regulate the global model's contribution during local updates. Experimental results demonstrate that FedDTRE can improve dialogue model performance and enhance the quality of dialogue generation.

翻译：随着人工智能的快速发展，对话系统已成为人机交互的重要形式。然而，由于数据隐私问题和异构设备能力的限制，传统的集中式或完全本地化训练方法在平衡隐私保护与个性化方面面临挑战。联邦学习作为一种代表性的分布式范式，提供了一种有前景的解决方案。然而，现有方法在有限的客户端数据下容易过拟合，并且在多轮训练后倾向于遗忘全局信息，导致泛化能力不佳。为解决这些问题，我们提出了FedDTRE，一种基于可信度评估的联邦自适应聚合策略，用于对话生成。FedDTRE并非直接用全局模型替换本地模型，而是利用全局模型和本地模型在一个面向公平性的评估数据集上的可信度得分，在本地更新过程中动态调节全局模型的贡献。实验结果表明，FedDTRE能够提升对话模型的性能，并提高对话生成的质量。