Hierarchical Federated Learning with Cluster Heads for Client Fairness
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Solution Overview
Problem
Federated learning systems face issues of client drift, reduced accuracy, non-convergence, and non-homogeneous performances due to heterogeneous data distributions, leading to unfairness among clients, which is critical for production applications where lower quantile accuracy affects user experience and company reputation.
Innovation Solution
A method and system that utilize hierarchical knowledge consensus by splitting the model into a global-shared encoder and cluster-shared prediction head, aggregating updates across clients to enhance fairness, using a p-Fairness metric that balances median and lower quantile accuracy, and employing cluster labels through K-means on latent features to protect privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional federated learning is used with heterogeneous data, then data privacy is preserved, but client drift and non-homogeneous performance occur leading to unfairness among clients
Solution Approach 1:
The model is segmented into two distinct parts: a global-shared encoder that captures universal features and a cluster-shared prediction head that adapts to specific data distributions. This segmentation allows different client clusters to have specialized prediction mechanisms while sharing common feature extraction capabilities, thereby resolving the contradiction between preserving data privacy through federated learning and achieving homogeneous performance across heterogeneous clients.
Solution Approach 2:
Instead of applying a uniform model structure to all clients, the system implements local quality by creating cluster-specific prediction heads tailored to different data distribution characteristics. Each cluster receives a customized prediction head that optimizes for its specific data patterns, while the global encoder maintains consistency across all clients. This approach ensures fair performance across clusters without compromising the privacy-preserving federated learning framework.
2Measurement precision
If model aggregation is performed across all clients, then global knowledge is captured, but performance of lower quantile clients deteriorates due to data heterogeneity
Solution Approach 1:
The aggregation process is segmented into two levels: global aggregation for the encoder that captures universal knowledge from all clients, and cluster-level aggregation for prediction heads that preserve specialized knowledge for specific data distributions. This dual-level segmentation prevents high-performing clients from dominating the global model updates, thereby protecting lower quantile clients while still achieving high global accuracy through the shared encoder.
Solution Approach 2:
The global-shared encoder acts as an intermediary that mediates between diverse client data distributions and the final prediction outputs. It captures common patterns across all clients without being overly influenced by any single cluster's characteristics, while cluster-specific prediction heads handle the nuances of individual data distributions. This intermediary structure ensures that global knowledge aggregation does not compromise the performance of lower quantile clients.
3Stability of the object's composition
If cluster-specific models are trained separately, then client accuracy homogeneity improves, but system complexity increases due to multiple model parameters
Solution Approach 1:
The global-shared encoder serves multiple functions: it acts as a universal feature extractor for all clients, a regularizer that prevents overfitting to cluster-specific patterns, and a knowledge aggregator that captures common patterns across diverse data distributions. This multi-functionality reduces system complexity by eliminating the need for completely separate models for each cluster, while still achieving homogeneous performance through the combination of the universal encoder and cluster-specific prediction heads.
Data Source
AI summary
A method of training neural networks with federated learning that includes sending portions of server-maintained machine learning models to clients, yielding local models in sync with the server; at each client, training a local model with local data, receiving a model parameter including a global-shared encoder and cluster-shared prediction head from the server, utilizing the cluster-shared prediction head for server aggregating models from clients in the respective cluster; at each client, syncing with the server on its locally updated model; at the server, updating the global-shared encoder by aggregating updates of the cross-entropy loss from clients; at the server, updating cluster-shared prediction heads by aggregating updates from clients in each cluster; at the server, sending updated global and cluster-shared model parameters to clients; repeating steps until a threshold is met; outputting a final parameter including a final global-shared encoder and cluster-shared model parameter for each cluster.