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New AugMP strategy targets federated fine-tuning of LLMs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new strategy called Augmented Model Manipulation (AugMP) to attack federated fine-tuning (FFT) of large language models (LLMs). This method uses graph representation learning to identify correlations in legitimate LLM updates, which then guides the creation of malicious updates. An iterative algorithm optimizes these malicious updates to embed adversarial objectives while appearing similar to benign updates, making them difficult to detect. Experiments show AugMP can significantly degrade global LLM accuracy and local agent performance while evading standard defense mechanisms. AI

IMPACT Introduces a novel attack vector that could compromise the integrity of LLMs trained via federated learning.

RANK_REASON Academic paper detailing a novel attack method on LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New AugMP strategy targets federated fine-tuning of LLMs

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Academic paper detailing a novel attack method on LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ozgur B. Akan ·

    Graph Representation Learning Augmented Model Manipulation on Federated Fine-Tuning of LLMs

    Federated fine-tuning (FFT) has emerged as a privacy-preserving paradigm for collaboratively adapting large language models (LLMs). Built upon federated learning, FFT enables distributed agents to jointly refine a shared pretrained LLM by aggregating local LLM updates without sha…

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