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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.08058 |
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| _version_ | 1866908584461205504 |
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| author | Lu, Shule Wang, Lingxiang Wen, Sijia Wang, Ziwei Zhang, Hainan |
| author_facet | Lu, Shule Wang, Lingxiang Wen, Sijia Wang, Ziwei Zhang, Hainan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_08058 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FedDTRE: Federated Dialogue Generation Models Powered by Trustworthiness Evaluation Lu, Shule Wang, Lingxiang Wen, Sijia Wang, Ziwei Zhang, Hainan Computation and Language Artificial Intelligence 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. |
| title | FedDTRE: Federated Dialogue Generation Models Powered by Trustworthiness Evaluation |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2510.08058 |