Privacy Failure in Split-LLM Training, The Returned Gradient Nullifies the Decoys

Researchers at arxiv. org discovered a critical privacy vulnerability in split-learning architectures where language models are distributed across multiple parties: returned gradients can be reverse-engineered to extract private training data, even when decoy data is introduced to mask sensitive information.

Researchers at arxiv.org discovered a critical privacy vulnerability in split-learning architectures where language models are distributed across multiple parties: returned gradients can be reverse-engineered to extract private training data, even when decoy data is introduced to mask sensitive information. This finding exposes a fundamental weakness in federated and split-LLM training setups commonly proposed for privacy-preserving collaborative AI development. The vulnerability is particularly relevant to regulated sectors deploying distributed LLMs across institutional boundaries.

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