SAFE-Guard: A Safety-Aware Federated Ecosystem for Guardrailing Large Language Models

Mohan Siva Krishna Konakanchi

doi:10.71097/IJSAT.v11.i4.9532

SAFE-Guard: A Safety-Aware Federated Ecosystem for Guardrailing Large Language Models

Author(s)	Mohan Siva Krishna Konakanchi
Country	United States
Abstract	The widespread deployment of Large Language Models (LLMs) has been accompanied by significant concerns regarding their potential to generate harmful, biased, or unsafe content. While various safety alignment techniques exist, they often lack dynamic adaptability and transparency. This paper introduces SAFE-Guard (Safety-Aware Federated Evaluation and Guardrailing), a comprehensive framework for regulating LLM outputs through a dynamic, learning-based approach. At the core of our framework is a ”Guardrail” model, a specialized LLM trained via Reinforcement Learning (RL) to inspect and act upon user prompts. The Guardrail learns a policy to allow, refuse, or safely rewrite prompts, moving beyond static keyword filters. To continuously improve this Guardrail on diverse and sensitive real-world data, we propose a Trust-Aware Federated Fine-Tuning (TFFT) protocol. This protocol ensures the integrity and accountability of the collaborative fine-tuning process by using a trust metric to weigh contributions from different data silos. Furthermore, we address the critical need for transparency by building a framework to quantify and optimize the tradeoff between the system’s safety performance (effectiveness at blocking harmful content while preserving helpfulness) and the explainability of its interventions. We validate SAFE-Guard on prominent safety benchmarks, demonstrating its superior ability to mitigate harmful generations while maintaining utility, its resilience in a federated setting, and its capacity to provide explainable safety controls.
Keywords	Large Language Models, AI Safety, Prompt Engineering, Reinforcement Learning, Federated Learning, Explainable AI (XAI).
Field	Engineering
Published In	Volume 11, Issue 4, October-December 2020
Published On	2020-10-08
DOI	https://doi.org/10.71097/IJSAT.v11.i4.9532
Short DOI	https://doi.org/hbb8hj

View / Download PDF File

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJSAT DOI prefix is
10.71097/IJSAT

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 16 Isu 3 Cover Page Vol 16 Isu 2 Cover Page Vol 16 Isu 1

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJSAT Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us	Message on WhatsApp	+91-9687-182-185	editor@ijsat.org

International Journal on Science and Technology

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

SAFE-Guard: A Safety-Aware Federated Ecosystem for Guardrailing Large Language Models

Share this