Federated Learning Simulator Variable Segmentation
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Solution Overview
Problem
Current federated learning system simulators lack the capability to simulate the optimization and aggregation of different types of variables, specifically locally optimized, local information, standard federated, and frozen variables, which are essential for determining optimal deployments of federated learning systems.
Innovation Solution
The proposed solution involves defining machine learning models with associated variables that are optimized and aggregated at edge nodes and a central node, utilizing a model simulation service that includes a simulation initializer to define variable lists and an aggregation map, allowing for the simulation of four types of variables: locally optimized, local information, standard, and frozen variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If federated learning systems are deployed to evaluate and research optimal configurations, then accurate simulation of variable optimization and aggregation is achieved, but deployment costs and resource consumption increase
Solution Approach 1:
The patent creates a virtual copy of the federated learning system through simulation. The simulation framework replicates the behavior of edge nodes, central nodes, and the federated learning process itself, allowing researchers to evaluate configurations without deploying actual hardware. This copying approach enables accurate measurement of system performance while avoiding the resource costs of physical deployment.
Solution Approach 2:
The simulation framework performs preliminary evaluation of federated learning configurations before actual deployment. By simulating variable optimization and aggregation processes in advance, the system identifies optimal configurations that can then be deployed with confidence, reducing the need for multiple costly deployment iterations and minimizing overall resource consumption.
2Adaptability or versatility
If current federated learning simulators are used, then basic simulation functionality is provided, but the capability to simulate different types of variables (locally optimized, local information, standard federated, and frozen variables) is lacking
Solution Approach 1:
The simulation framework segments variables into four distinct types: locally optimized variables, local information variables, standard federated variables, and frozen variables. Each variable type is handled through dedicated simulation components that implement specific optimization and aggregation behaviors appropriate to that variable category, enabling comprehensive and flexible simulation capabilities.
Solution Approach 2:
The simulation framework is designed as a universal platform capable of simulating multiple variable types and federated learning configurations through a single integrated system. The framework can adapt to different simulation scenarios by configuring which variable types are active and how they interact, providing versatile simulation functionality without requiring separate specialized simulators for each variable type.
Data Source
AI summary
One example method includes, for each federated learning simulation, defining a machine learning model that is used in the federated learning simulation. The machine learning model has associated variables and is implemented at edge nodes and a central node of the federated learning simulation. A first variable list is defined that specifies associated variables that are to be optimized at the edge nodes of the federated learning simulation. A second variable list is defined that specifies associated variables that are to be provided by the edge nodes to the central node of the federated learning simulation. The associated variables included in the first variable list are optimized at the edge nodes of federated learning simulation. The associated variables that are included in the second variable list and that are provided by the edge nodes of the federated learning simulation are aggregated by the central node of the federated learning simulation.