Responsible Federated LLMs via Safety Filtering and Constitutional AI
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918506841243648 |
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| author | Noh, Eunchung Baek, Jeonghun |
| author_facet | Noh, Eunchung Baek, Jeonghun |
| contents | Recent research has increasingly focused on training large language models (LLMs) using federated learning, known as FedLLM. However, responsible AI (RAI), which aims to ensure safe and trustworthy responses, remains underexplored in this context. In FedLLM, client-side training data may contain harmful content, resulting in unsafe LLMs that can generate inappropriate responses. Aggregating such models into a global model and redistributing it to clients risks the widespread deployment of unsafe LLMs. To address this, we incorporate two well-established RAI techniques into FedLLM: safety filtering and constitutional AI. Our experiments show that these methods significantly improve LLM safety, achieving over 20% improvement on AdvBench. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16691 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Responsible Federated LLMs via Safety Filtering and Constitutional AI Noh, Eunchung Baek, Jeonghun Computation and Language Distributed, Parallel, and Cluster Computing Multiagent Systems Recent research has increasingly focused on training large language models (LLMs) using federated learning, known as FedLLM. However, responsible AI (RAI), which aims to ensure safe and trustworthy responses, remains underexplored in this context. In FedLLM, client-side training data may contain harmful content, resulting in unsafe LLMs that can generate inappropriate responses. Aggregating such models into a global model and redistributing it to clients risks the widespread deployment of unsafe LLMs. To address this, we incorporate two well-established RAI techniques into FedLLM: safety filtering and constitutional AI. Our experiments show that these methods significantly improve LLM safety, achieving over 20% improvement on AdvBench. |
| title | Responsible Federated LLMs via Safety Filtering and Constitutional AI |
| topic | Computation and Language Distributed, Parallel, and Cluster Computing Multiagent Systems |
| url | https://arxiv.org/abs/2502.16691 |