Hierarchical Federated Learning with Cluster-Shared Heads for Fairness
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Solution Overview
Problem
Federated learning systems face issues of client drift, reduced accuracy, non-convergence, and unfairness due to heterogeneous data distributions, leading to significant performance differences among clients, which can negatively impact user experiences and company reputation.
Innovation Solution
A hierarchical consensus approach is implemented, where models are split into a global-shared encoder and cluster-shared prediction head, with cluster labels estimated using k-means on latent features, allowing for both global and cluster-wise knowledge consensus, and updating model parameters to enhance fairness and accuracy across clients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional federated learning is used with heterogeneous data, then client drift and non-convergence occur, but model accuracy and system reliability deteriorate
Solution Approach 1:
The model is segmented into encoder and prediction head components, allowing differential updating strategies. The encoder captures global patterns while prediction heads adapt to local cluster characteristics, resolving the contradiction between convergence stability and accuracy by enabling both global consistency and local adaptation.
Solution Approach 2:
Different parts of the system (encoder vs. prediction head) are optimized with different qualities - the encoder maintains global stability through aggregated updates while prediction heads achieve local accuracy through cluster-specific adaptation. This local quality differentiation resolves the contradiction between overall convergence and local performance.
2Manufacturing precision
If cluster-based hierarchical model is implemented, then fairness among clients improves, but device complexity increases
Solution Approach 1:
The hierarchical model segments parameters into encoder and prediction head components with different update rules. This segmentation enables fairness by allowing cluster-specific adaptation while maintaining global consistency, achieving performance fairness without requiring complete model duplication across clients.
Solution Approach 2:
The encoder serves as a universal component shared across all clients and clusters, capturing global patterns. The prediction heads provide cluster-specific functionality. This multi-functionality approach achieves fairness while controlling complexity through parameter sharing.
3Manufacturing precision
If k-means clustering on latent features is used, then client groupings improve fairness, but loss of information occurs during feature extraction
Solution Approach 1:
Latent features serve as an intermediary representation that captures essential client characteristics for clustering while preserving data privacy. The k-means algorithm clusters based on these compressed features rather than raw data, achieving accurate client grouping without direct access to sensitive information.
Solution Approach 2:
The system extracts only the necessary clustering information from raw data through latent feature representation. This extraction approach enables accurate client grouping for fairness while leaving sensitive information behind, minimizing information loss while achieving the clustering objective.
Data Source
AI summary
A computer-implemented method for training neural networks with federated learning that includes sending portions of server-maintained machine learning models to clients, using local models without cluster labels at clients, estimating cluster labels at the server using k-means on latent features from clients, training local models with client data using a global-shared encoder parameter and a cluster-shared prediction head, updating the global encoder parameter at the server by aggregating cross-entropy loss updates from clients, updating cluster-shared prediction heads at the server by aggregating updates from clients within each cluster, sending updated global and cluster-shared model parameters to clients, and outputting a final parameter, including a global-shared encoder and cluster-shared model parameter, after meeting a threshold.