Federated Learning with Distribution-Matched Model Selection
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Solution Overview
Problem
Federated learning systems face challenges due to non-independent and identically distributed data across clients, leading to poor model performance and convergence issues.
Innovation Solution
Implement a federated learning method that utilizes prior and posterior distributions of model parameters to align data distributions across nodes, selecting models closest to local data distributions for training, and reducing communication overhead through parameter sparsification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional federated learning is used with non-IID data across clients, then data privacy is protected, but model performance deteriorates and convergence fails
Solution Approach 1:
The patent segments the global model into multiple candidate models with different parameter distributions. Each client selects and trains on the candidate model that best matches its local data distribution characteristics, rather than using a single global model for all clients. This segmentation allows each client to adapt to its specific data characteristics while maintaining data privacy.
Solution Approach 2:
The patent implements local quality by allowing each client to choose a candidate model tailored to its local data distribution. Instead of imposing a uniform global model, the system enables each client to work with a model that has parameter distributions optimized for its specific data characteristics, thereby improving local model performance while preserving data privacy.
2Reliability
If multiple rounds of model aggregation are performed to achieve convergence, then model accuracy improves, but communication overhead increases
Solution Approach 1:
The patent performs preliminary action by pre-generating multiple candidate models with different parameter distributions before the federated learning process begins. This preparation allows clients to immediately select and train on appropriate candidate models without requiring multiple iterative aggregation rounds to discover suitable model configurations, thereby reducing communication overhead while maintaining model accuracy.
3Reliability
If all model parameters are transmitted for aggregation, then model convergence is achieved, but communication cost increases
Solution Approach 1:
The patent extracts and transmits only the essential information needed for model aggregation by focusing on parameter distribution characteristics rather than all individual parameter values. Clients select and train on candidate models whose parameter distributions match their local data, reducing the amount of data that needs to be transmitted and processed during aggregation while still achieving model convergence.
Data Source
AI summary
A federated learning method is provided, applied to the field of artificial intelligence technologies. In the method, federated learning is implemented by exchanging prior distribution and posterior distribution of a model parameter between nodes, so that data distribution of training data in the nodes can be learned in a model training process. In addition, when obtaining a plurality of models corresponding to different data distribution, the node selects, from the plurality of models based on performance of each model in processing training data, a model closest to a training data distribution for training. This resolves a problem that training data distribution on different nodes is different, and can effectively improve effect of a model obtained through training.