Responsible Federated LLMs via Safety Filtering and Constitutional AI

2023-03-30 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Expert, extended

Summary

Research into Federated Large Language Models (FedLLM) has largely overlooked Responsible AI (RAI) principles, leading to potential safety issues where models trained on client data containing harmful content can generate unsafe responses. This study integrates two established RAI methods, a safety filter and Constitutional AI (CAI), into the FedLLM framework to mitigate these risks. Experiments demonstrate that incorporating these methods significantly enhances the safety of LLMs, achieving over a 20% improvement on the AdvBench safety evaluation benchmark. The safety filter, specifically Llama Guard 3 (LG3) finetuned on the S-LG20K dataset, prevents harmful data from being used in local model training. CAI, applied to the global model, guides the LLM to self-critique and revise responses according to constitutional guidelines, with a cost-efficient approach reducing computational overhead by 96%.

Key takeaway

For research scientists developing or deploying FedLLMs, you should prioritize integrating Responsible AI methods from the outset. Incorporating a finetuned safety filter like Llama Guard 3 for client data and Constitutional AI for global model refinement can significantly improve safety performance, as demonstrated by over 20% gains on AdvBench. This proactive approach is crucial to prevent the widespread deployment of unsafe models and ensure ethical AI development.

Key insights

Integrating safety filters and Constitutional AI significantly improves FedLLM safety against harmful content.

Principles

• Client data can introduce harmful content.
• RAI methods enhance LLM safety.
• Combined methods offer complementary safety benefits.

Method

The proposed method applies a finetuned Llama Guard 3 safety filter to client data before local training and a cost-efficient Constitutional AI to the global model before distribution, using supervised finetuning and direct preference optimization.

In practice

• Use Llama Guard 3 as a safety filter.
• Implement Constitutional AI for self-critique.
• Apply PEFT (LoRA) for FedLLM efficiency.

Topics

• Federated Learning
• Large Language Models
• Responsible AI
• Safety Filter
• Constitutional AI

Code references

• tatsu-lab/stanford_alpaca

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.