PROTECTED TRAINING OF PRIVATE ADAPTER MODELS FOR A HOSTED FOUNDATION MODEL

원문 (중국어)

Methods and systems are provided for training copies of a private adapter network at respective client computing devices; and aggregating of trained weight sets in a common parameter space as a weight set of a hosted foundation model at a cloud computing system. A private adapter model can be a subdivision of a hosted foundation model, segmented from some number of layers of a hosted foundation model or can be distinct from the hosted foundation model, given that the private adapter model configures a computing host to update a weight set in a common parameter space as a weight set of the hosted foundation model. By performing a protected update to a weight set, true values of the coefficients of the weight set derived from inputting features of a labeled dataset at a first layer of the private adapter model are obfuscated.