Secure Multiparty Protocol for Fine-tuning of Language Models
AssigneeOracle International Corporation
InventorsWei Jiang, Arisa Tajima, Virendra J. Marathe, Adam C. Pocock
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Abstract
Systems and methods for implementing a secure multiparty protocol for fine-tuning of language models are disclosed. An end-to-end privacy-preserving protocol using secure multi-party computation (MPC) and executed on a plurality of computing nodes enables fine-tuning a language model targeting classification tasks using private, sensitive data while providing secure protection of the training data and without sacrificing model accuracy.
Claims20
A system, comprising:
- a plurality of computing nodes individually comprising at least one processor and memory, the plurality of computing nodes configured to communicate using a privacy-preserving fine-tuning protocol to create a fine-tuned large language model (LLM) according to one or more hyperparameters, wherein to create the fine-tuned LLM the plurality of computing nodes are configured to:
- derive the fine-tuned LLM from a pretrained LLM according to the one or more hyperparameters, wherein to derive the fine-tuned LLM the plurality of computing nodes are configured to:
- freeze at least a portion of the pretrained LLM;
- and
- configure a remaining portion of the fine-tuned LLM according to the one or more hyperparameters and the privacy-preserving fine-tuning protocol;
- receive respective training information from individual clients of a plurality of clients, wherein secrecy of the respective training information is preserved with respect to individual nodes of the plurality of computing nodes;
- and
- fine tune the remaining portion of the fine-tuned LLM according to the respective secret training information and the privacy-preserving fine-tuning protocol.
A method comprising:
- creating, by a plurality of computing nodes communicating using a privacy-preserving fine-tuning protocol, a fine-tuned large language model (LLM) according to one or more hyperparameters, the creating comprising:
- deriving the fine-tuned LLM from a pretrained LLM according to the one or more hyperparameters, the deriving comprising:
- freezing at least a portion of the pretrained LLM;
- and
- configuring a remaining portion of the fine-tuned LLM according to the one or more hyperparameters and the privacy-preserving fine-tuning protocol;
- receiving respective training information from individual clients of a plurality of clients, wherein secrecy of the respective training information is preserved with respect to individual nodes of the plurality of computing nodes;
- and
- fine tuning the remaining portion of the fine-tuned LLM according to the respective secret training information and the privacy-preserving fine-tuning protocol.
One or more non-transitory, computer-readable storage media, storing program instructions that when executed on or across one or more processors cause the one or more processors to perform:
- implementing a node of a plurality of computing nodes communicating according to privacy-preserving fine-tuning protocol to create a fine-tuned large language model (LLM) according to one or more hyperparameters, the creating comprising:
- deriving the fine-tuned LLM from a pretrained LLM according to the one or more hyperparameters, the deriving comprising:
- freezing at least a portion of the pretrained LLM;
- and
- configuring a remaining portion of the fine-tuned LLM according to the one or more hyperparameters and the privacy-preserving fine-tuning protocol;
- receiving respective training information from individual clients of a plurality of clients, wherein secrecy of the respective training information is preserved with respect to individual nodes of the plurality of computing nodes;
- and
- fine tuning the remaining portion of the fine-tuned LLM according to the respective secret training information and the privacy-preserving fine-tuning protocol.
Dependencies are inferred from the claim text.