Federated Learning Clustering via Feature Distribution Similarity
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Solution Overview
Problem
Conventional federated learning systems lack effective methods to quantify feature distribution similarity among participating nodes, leading to degraded performance, as they do not consider the degree of quantification of feature distribution similarity, making it difficult to predict the accuracy of AI models trained on one dataset when tested on another.
Innovation Solution
A distributed clustering-based multi-layer federated learning method that calculates feature distribution similarity among non-IID data nodes, groups them into clusters using unsupervised learning techniques like K-means or Gaussian Mixture Models, and updates local models within these clusters using a federated learning algorithm, with the aggregation method supported by blockchain technology to enhance data privacy and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional federated learning systems use centralized server aggregation without quantifying feature distribution similarity, then the system structure remains simple, but the learning performance is severely degraded
Solution Approach 1:
The patent applies preliminary action by calculating feature distribution similarity metrics (such as statistical distance measures) before the model aggregation process. This allows the system to pre-identify and group nodes with similar feature distributions, ensuring that aggregation operations are performed on compatible nodes, thereby preventing performance degradation from the outset
Solution Approach 2:
The patent segments the federated learning system into multiple layers based on feature distribution similarity. Nodes are first grouped into clusters with similar feature characteristics, and then aggregation is performed within these clusters rather than globally. This segmentation approach maintains system structure while improving learning performance by avoiding mismatched aggregations
2Measurement precision
If nodes are grouped into clusters based on feature distribution similarity, then model accuracy is improved, but the computational overhead increases
Solution Approach 1:
The patent applies partial action by calculating feature distribution similarity metrics selectively rather than for all possible node pairs. The system computes similarity measures only for necessary clustering decisions, and performs aggregation operations at the cluster level rather than individual node level, reducing overall computational overhead while maintaining accuracy improvements
Solution Approach 2:
The patent substitutes complex mechanical-style exhaustive comparison with statistical similarity measures. Instead of performing full model comparisons or extensive communications to assess node compatibility, the system uses efficient statistical metrics (such as moment-based or divergence-based measures) to quickly evaluate feature distribution similarity, reducing computational overhead
3Reliability
If multi-layer aggregation is implemented to improve model performance, then learning effectiveness increases, but the aggregation process time increases
Solution Approach 1:
The patent segments the aggregation process into multiple layers based on feature distribution similarity. The first layer performs aggregation within clusters of nodes with similar features, and subsequent layers aggregate cluster-level models. This segmentation allows parallel processing within layers and reduces the overall aggregation time compared to sequential global aggregation, while maintaining learning effectiveness
Data Source
AI summary
A multi-layer federated learning method based on distributed clustering is provided, which comprises the following steps. Computing a feature distribution similarity for each of participating nodes with non-(non-independent and identically distributed) data sets, and grouping these nodes into plural clusters by the feature distribution similarity. Updating local model of nodes of each cluster by a federated learning algorithm, and inputting these nodes into a multi-layer aggregation mechanism. Terminating the operation of the multi-layer aggregation mechanism until the clustering result meets a required demand. Furthermore, we implement a blockchain-based multi-layer federated learning system, including model aggregation module, API module, time-series synchronization module, and IPFS, based on distributed clustering architecture. The learning performance is proven to effectively improved.