Adaptive Personalized Federated Learning for Heterogeneous Models
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Solution Overview
Problem
Existing federated learning methods face challenges with data heterogeneity and model heterogeneity, leading to inefficiencies and accuracy issues, particularly when dealing with non-IID data distributions and diverse model structures across participants.
Innovation Solution
An adaptive personalized federated learning method that supports heterogeneous models, where participants update both private and global shared models using stochastic gradient descent, and the central server aggregates model parameters to ensure convergence and adaptability across different data heterogeneity levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional federated averaging method is used, then implementation is simple, but convergence is slow or divergence occurs when data distribution is heterogeneous
Solution Approach 1:
The patent applies local quality by allowing each participant to maintain a personalized model structure adapted to their local data characteristics while still participating in federated learning. Each participant's model has unique parameters tailored to their data distribution, enabling effective convergence on heterogeneous data without sacrificing implementation feasibility through the structured personalization approach.
2Measurement precision
If personalized federated learning methods are applied, then accuracy improves on heterogeneous data, but adaptability to different heterogeneity levels deteriorates
Solution Approach 1:
The patent implements dynamics by introducing a dynamic adaptation mechanism where the system automatically adjusts the degree of personalization based on the measured level of data heterogeneity. When heterogeneity is high, the system increases personalization to improve accuracy, while when heterogeneity is low, it reduces personalization to maintain versatility across different scenarios.
3Device complexity
If homogeneous model structure is enforced, then federated learning implementation is straightforward, but model privacy and personalization capability are reduced
Solution Approach 1:
The patent applies segmentation by dividing the model into shared components and private components. The shared components maintain a common structure for federated learning, while the private components are segmented and kept local to each participant, preserving model privacy. This segmentation allows straightforward implementation of the shared portion while enhancing personalization and privacy through the private segments.
4Measurement precision
If private model and global shared model are both trained, then model accuracy improves, but training complexity and computational overhead increase
Solution Approach 1:
The patent applies merging by integrating the training of private models and global shared models into a unified federated learning framework. Instead of treating them as separate training processes, the system combines their objectives into a single optimization problem where both model types are updated simultaneously through coordinated parameter adjustments, reducing training complexity while maintaining accuracy benefits.
Data Source
AI summary
The present invention discloses an adaptive personalized federated learning method supporting heterogeneous model, based on the use of models with different structures by various participants supporting federated learning, learning the dynamic weight used for model ensemble and introducing optimization objectives for model integration in the process of training model parameters, realizing highly accurate personalized federated learning with heterogeneous and self adaptive data, the participants are enabled to benefit from federated learning in scenes with heterogeneous data at different levels. The adaptive personalized federated learning method of the present invention does not need to introduce new hyper parameters, and can be conveniently deployed in the existing federated learning system; comparing with the traditional personalized federated learning method, the present invention has stronger adaptability.