Federated Model Deployment via Data Distillation
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Solution Overview
Problem
Data nodes lack the necessary computing resources to train prediction models, leading to the transmission of large data quantities, which poses privacy and security risks and inefficient resource usage, especially as data node clusters grow.
Innovation Solution
Implementing a data node manager that uses federated learning and dataset distillation to deploy prediction models by generating a distilled dataset for each data node cluster, allowing models to be deployed without transmitting large data quantities between data nodes and the manager, and using these datasets to efficiently redeploy models as clusters expand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large quantities of data are transmitted between computing devices for model training, then model training capability is improved, but privacy and security risks increase
Solution Approach 1:
The patent extracts only the essential information needed for model training by generating distilled datasets that capture statistical properties of the original data without containing actual data samples. This allows model training to proceed while sensitive data never leaves the local devices, eliminating privacy and security risks associated with data transmission.
Solution Approach 2:
The patent introduces distilled datasets as an intermediary representation between the original data and the model training process. These distilled datasets serve as a mediator that preserves the statistical properties needed for training while removing the actual sensitive data, thus enabling training without direct data exposure.
2Measurement precision
If large quantities of data are transmitted for model training, then training accuracy is improved, but resource usage efficiency deteriorates
Solution Approach 1:
The patent extracts only the statistical properties and distribution characteristics from the original large datasets to create distilled datasets. This extraction process retains the essential information for training accuracy while dramatically reducing the data volume that needs to be transmitted and processed, thereby improving resource usage efficiency.
Solution Approach 2:
The patent changes the representation of data from raw data samples to distilled statistical representations. This parameter transformation maintains the information content necessary for training accuracy while reducing the data size and transmission requirements, thus optimizing resource consumption.
3Adaptability or versatility
If data node clusters grow, then service coverage is improved, but data transmission requirements increase
Solution Approach 1:
The patent performs preliminary action by pre-computing and caching distilled datasets for different data node clusters before new nodes are added. When a new node joins the cluster, the system can immediately use the pre-computed distilled datasets for that cluster, avoiding the need to transmit and reprocess large amounts of data for the new node.
Solution Approach 2:
The patent extracts and stores distilled datasets that capture the statistical properties of each data node cluster. These distilled datasets can be efficiently transmitted to new nodes or reused across multiple nodes, significantly reducing the data transmission requirements as clusters grow compared to transmitting raw data for each new node.
Data Source
AI summary
Techniques described herein relate to a method for managing data nodes of data node clusters. The method includes obtaining, by a data node manager, a request to deploy a model to a data node; in response to obtaining the model deployment request: identifying, by the data node manager, a data node cluster associated with the data node; making a first determination, by the data node manager, that the data node cluster is associated with an available distilled dataset; and in response to the first determination: generating, by the data node manager, a model using the available distilled dataset; and deploying, by the data node manager, the model to the data node.