Federated Learning Feature Normalization Layer
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Solution Overview
Problem
Federated learning systems face challenges due to data heterogeneity among clients, leading to poor performance and increased training requirements, as the distribution of data varies significantly across local datasets, resulting in issues like label shift.
Innovation Solution
Incorporating a normalization layer in the machine learning model architecture that normalizes feature vectors based on their magnitude or a threshold value, helping to mitigate data heterogeneity and improve model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is used to train a model with data from multiple clients, then data privacy is protected and large amounts of local data can be utilized, but data heterogeneity causes poor model performance and increased training requirements
Solution Approach 1:
The patent applies parameter changes by introducing a normalization layer that modifies the feature vector parameters (magnitude and direction) before they are used for classification. This normalization transforms the feature representation to be independent of the specific client's data distribution, thereby improving model performance across heterogeneous data while maintaining the federated learning framework's ability to handle diverse data sources
2Productivity
If feature normalization is applied to mitigate data heterogeneity, then model performance improves and training rounds are reduced, but the model architecture becomes more complex
Solution Approach 1:
The normalization layer serves as an intermediary component between the feature extraction subnetwork and the final classification layer. It processes the feature vectors by normalizing their magnitude and adjusting their direction, acting as a mediator that harmonizes the representations from different clients before they are used for training, thereby improving training efficiency without requiring fundamental architectural changes
Data Source
AI summary
Methods and systems for federated learning using feature normalization are disclosed. A client implements a local model including at least: a feature extraction subnetwork to extract a feature vector from input data, a normalization layer to normalize the feature vector, and a final layer to generate a prediction output from the normalized feature vector. The local model is initialized using a set of global parameters received from a central server. The local model is updated using data sampled from a local dataset. Information about a state of the updated local model is transmitted to the central server.