Federated Learning Parameter Fusion via Dynamic Evaluation
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Solution Overview
Problem
In federated learning, the fusion of local model parameters among data holders is not effectively integrated, leading to suboptimal generalization of federated recognition models due to inadequate parameter fusion methods.
Innovation Solution
A method that acquires local model parameters from multiple clients, applies various parameter fusion modes to these parameters, evaluates the resulting global models using a multimedia verification dataset, and selects the optimal global model for updating local recognition models, thereby enhancing the fusion efficiency and generalization of object recognition models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If parameter mean is performed on local model parameters to obtain a total model, then the federated learning process can be supported with distributed data, but the local model parameters are less effectively fused, affecting the generalization effect of federated models
Solution Approach 1:
The patent transforms the single parameter mean approach into multiple parameter fusion modes (uniform fusion, importance-weighted fusion, and curriculum fusion). Each mode applies different weighting strategies to local model parameters, changing how parameters are aggregated. The curriculum fusion mode dynamically adjusts weights based on client data quality and model performance, enabling adaptive parameter fusion that improves both fusion effectiveness and generalization.
Solution Approach 2:
The patent introduces dynamic weight adjustment mechanisms in the curriculum fusion mode, where weights assigned to different clients' parameters change based on their data quality assessments and model performance metrics. This dynamic approach allows the system to adaptively optimize parameter fusion effectiveness across different training stages and client configurations, resolving the contradiction between fusion effectiveness and generalization.
2Manufacturing precision
If multiple parameter fusion modes are applied to local model parameters, then the effectiveness of parameter fusion is improved, but the complexity of the model training process increases
Solution Approach 1:
The patent segments the parameter fusion process into distinct modes (uniform fusion, importance-weighted fusion, and curriculum fusion), each handling different aspects of parameter aggregation. This segmentation allows the system to apply appropriate fusion strategies to different components of the training process, improving overall fusion effectiveness while managing complexity through modular design.
Solution Approach 2:
The patent implements curriculum fusion mode that selectively applies weighted fusion to important parameters while using uniform fusion for less critical ones. This partial application of complex fusion strategies reduces overall process complexity while maintaining effectiveness for the most impactful parameters.
3Reliability
If local model parameters are periodically uploaded to a server for fusion, then federated learning can be performed with data privacy preserved, but the fusion of parameters from different data holders is not effectively integrated
Solution Approach 1:
The patent changes the parameter aggregation methodology from simple mean to multiple fusion modes that consider data quality, client reliability, and parameter importance. This transformation enables effective parameter integration while maintaining the federated learning framework that preserves data privacy through periodic uploads.
Solution Approach 2:
The server acts as an intermediary that receives local model parameters from multiple clients and applies sophisticated fusion modes to integrate them effectively. This intermediary role enables the system to balance data privacy requirements with effective parameter integration by performing weighted aggregation based on data quality assessments and model performance metrics.
Data Source
AI summary
The present disclosure relates to data processing method and apparatus. The method includes: acquiring local model parameters respectively corresponding to N local recognition models; acquiring M parameter fusion modes associated with a local model parameter set, and performing parameter fusion on the local model parameter set respectively according to each parameter fusion mode, so as to obtain M alternative global models; and acquiring evaluation indexes of the M alternative global models respectively in a multimedia verification data set, determining a target global model in the M alternative global models according to the evaluation indexes, and transmitting the target global model to N clients, the N clients updating parameters of a local recognition model associated with the target global model according to the target global model respectively, so as to obtain an object recognition model.