Federated Learning Node Selection Under Data Heterogeneity
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Solution Overview
Problem
Existing collaborative machine learning (CML) systems face challenges in ensuring dataset similarity among distributed computing devices, leading to sub-optimal model convergence due to variations in data distributions, which can be exacerbated by differences in user accents, background noise, and device heterogeneity.
Innovation Solution
A method for determining dataset similarity using metrics like Wasserstein Distance (WD) and Maximum Mean Discrepancy (MMD) to select processing nodes for model updates, employing a hierarchical representation to cluster nodes with similar data distributions, and optimizing data transmission based on network bandwidth and node resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If processing nodes with different data distributions are included in collaborative learning, then system scalability and participation are improved, but model convergence and accuracy deteriorate due to data heterogeneity
Solution Approach 1:
The patent segments processing nodes into clusters based on data distribution similarity. Each cluster contains nodes with homogeneous data characteristics, allowing independent model training within clusters while maintaining overall system scalability. This segmentation resolves the contradiction by enabling large-scale participation without compromising convergence through intra-cluster homogeneity.
Solution Approach 2:
The patent applies local quality by training separate models for different clusters with distinct data distributions. Each cluster receives customized model parameters optimized for its specific data characteristics, while the system maintains a unified architecture. This allows diverse data types to coexist in the system without interfering with each other's convergence.
2Measurement precision
If dataset similarity metrics are computed for all processing nodes, then node selection accuracy is improved, but computational overhead and time consumption increase
Solution Approach 1:
The patent performs preliminary clustering of processing nodes based on data distribution similarity before the main model training process. This preliminary action groups nodes into clusters with homogeneous characteristics, enabling faster and more accurate node selection during subsequent training rounds without requiring comprehensive pairwise comparisons of all nodes.
Solution Approach 2:
Instead of computing similarity metrics for all possible node pairs (excessive action), the patent computes metrics only for nodes within the same cluster (partial action). This selective computation maintains sufficient selection accuracy while dramatically reducing computational overhead and time consumption.
3Reliability
If all processing nodes participate in model updates, then model accuracy is improved through diverse data, but network bandwidth requirements and communication overhead increase
Solution Approach 1:
The patent segments participating nodes into clusters and trains models independently within each cluster. This segmentation allows the system to achieve high model accuracy through diverse data from multiple clusters while reducing total communication overhead, as only cluster-level aggregates need to be transmitted rather than all individual node data.
Solution Approach 2:
The patent merges the results from multiple cluster models into a unified system model. By combining the strengths of different clusters, the system achieves high overall accuracy while minimizing the total data transmission volume required, as only the aggregated cluster results need to be communicated rather than all individual node contributions.
Data Source
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AI summary
This specification describes an apparatus relating to collaborative machine learning, or federated learning. The apparatus may comprise means for determining one or more properties associated with one or more processing nodes, the one or more processing nodes configured to utilize respective data based on a local dataset of one or more particular processing nodes for updating a collaboratively learned model. The apparatus may also comprise means for determining, based on the one or more properties, one or more of the particular processing nodes for use in updating the learned model.