FedDTRE: Federated Dialogue Generation Models Powered by Trustworthiness Evaluation
This addresses privacy and personalization challenges in dialogue systems for users and developers, but it is incremental as it builds on existing federated learning methods.
The paper tackles the problem of poor generalization in federated learning for dialogue generation due to overfitting and forgetting global information, proposing FedDTRE which uses trustworthiness evaluation to dynamically adjust global model contributions, resulting in improved performance and dialogue quality.
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.