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AssigneeHong Kong Applied Science and Technology Research Institute Company Limited
InventorsJitao Ou, Jiazheng Yan, Wenjun Zhuang, Kam Hong Shum
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Embodiments of the present disclosure provide hierarchical, differential privacy enhancements to federated, machine learning. Local machine learning models may be generated and/or trained by data owners participating in the federated learning framework based on their respective data sets. Noise corresponding to and satisfying a first privacy loss requirement are introduced to the data owners' respective data sets, and noise corresponding to and satisfying a first privacy loss requirement are introduced to the local models generated and/or trained by the data owners. The data owners transmit model data corresponding to their respective local models to a coordinator, which in turn aggregates the data owners' model data. After introducing noise corresponding to and satisfying a third privacy loss requirement to the aggregated model data, the coordinator transmits the aggregated model data to the data owners to facilitate updating and/or re-training on their respective machine learning models.
A method of applying dynamic differential privacy enhancements to federated learning, comprising:
A method of applying dynamic differential privacy enhancements to federated learning, comprising:
A non-transitory computer-readable storage medium storing instructions that, when executed by one or more first processors, cause the one or more first processors to perform operations for applying dynamic differential privacy enhancements to federated learning, the operations comprising:
Dependencies are inferred from the claim text.