Federated Learning Aggregation Across Heterogeneous Model Structures
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Federated learning methods struggle to aggregate models of different network structures, leading to difficulties in implementing model aggregation and compromising prediction precision.
Innovation Solution
A federated learning method that groups models with the same network structure together for parameter aggregation and performs knowledge distillation training to integrate knowledge and experience across models of varying structures, enhancing prediction precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If models of different network structures are trained on different clients, then model adaptability to local data is improved, but model aggregation becomes difficult to implement
Solution Approach 1:
The patent segments models into two distinct parts: structure parameters (network architecture) and weight parameters (model weights). This segmentation allows different clients to train models with different network structures while enabling the aggregation node to aggregate only the weight parameters, thus resolving the contradiction between model adaptability and aggregation complexity
Solution Approach 2:
The patent extracts and separates the weight parameters from the overall model structure. By taking out the weight parameters as the aggregatable component, the system enables federated learning aggregation while preserving the ability to use different network structures on different clients, solving the aggregation difficulty problem
2Device complexity
If parameter aggregation is performed on models of different network structures, then model aggregation is enabled, but prediction precision deteriorates
Solution Approach 1:
The patent introduces an intermediary mechanism (the aggregation node) that receives models from multiple clients, performs weight parameter aggregation, and then uses knowledge distillation to transfer knowledge from original models to the aggregated model. This intermediary process ensures both aggregation feasibility and prediction precision are maintained
Solution Approach 2:
The patent changes the aggregation approach from direct structure parameter aggregation to weight parameter aggregation, and further refines it by using knowledge distillation to optimize the aggregated model's parameters. This parameter transformation enables aggregation while preserving or improving prediction precision
3Measurement precision
If knowledge distillation training is performed on aggregation models, then prediction precision is improved, but training time increases
Solution Approach 1:
The patent performs weight parameter aggregation before knowledge distillation training. This preliminary aggregation creates a preliminary aggregated model that serves as a good starting point for knowledge distillation, reducing the number of training iterations needed and thus decreasing overall training time while maintaining precision improvement
Data Source
AI summary
A federated learning method is provided, applied to the field of artificial intelligence technologies. According to the method, when obtaining models of different network structures, an aggregation node groups models of a same network structure into a same group, and performs parameter aggregation on models in a same group, to obtain a plurality of aggregation models of different network structures. In addition, for each aggregation model, knowledge distillation training is performed on each aggregation model based on the plurality of originally obtained models, to implement experience transfer between the models of different network structures, so as to integrate knowledge and experience of models of various network structures, combine advantages of parameter aggregation and knowledge distillation in integrating model experience, implement aggregation of the models of different network structures, and ensure prediction precision of a model obtained through aggregation.