通过可信度评估动态调整联邦对话模型更新，提升隐私保护下的生成质量。
FedDTRE: Federated Dialogue Generation Models Powered by Trustworthiness Evaluation
- 基于可信度评分动态调节全局模型贡献
- 在有限数据下减少过拟合，提升对话生成效果
- 适合注重隐私与个性化平衡的对话系统开发者
随着人工智能快速发展，对话系统已成为人机交互的重要形式。然而，传统的集中式或完全本地训练方法在数据隐私与个性化之间难以平衡，且受限于设备能力差异。联邦学习作为分布式范式提供了潜在解决方案，但现有方法在客户端数据有限时易出现过拟合，多轮训练后常遗忘全局信息，导致泛化性能差。为此，我们提出 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。该方法不直接用全局模型替换本地模型，而是利用全局与本地模型在公平性导向评估数据集上的可信度评分，动态调节全局模型在本地更新中的贡献。实验表明，FedDTRE可有效提升对话模型性能，增强对话生成质量。
原文摘要 · Abstract (English)
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.
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