Auto-Tuned Swarm Learning Hyperparameter Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Distributed machine learning systems face accuracy issues due to the heterogeneity of data across nodes, as they typically use a common algorithm for training, which can lead to less accurate predictions since nodes are not aware of other nodes' local data.
Innovation Solution
Implementing an auto-tuned swarm learning approach that optimizes hyperparameters at local nodes during training, merging local model parameters and hyperparameters to create a global model that reflects the most effective configuration across nodes, using blockchain technology for decentralized management and secure data sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a common algorithm is used for training across all nodes, then system simplicity is maintained, but model accuracy deteriorates due to data heterogeneity
Solution Approach 1:
The patent applies local quality by allowing each node to have its own optimized hyperparameters tailored to its local data characteristics while maintaining a common model architecture. This enables each node to adapt the global model to local conditions, improving accuracy without requiring completely different algorithms at each node.
Solution Approach 2:
The system dynamically adjusts hyperparameters at each node based on local data characteristics during the training process. The hyperparameter optimization is performed locally at each node, allowing the system to adapt to data heterogeneity dynamically rather than using static uniform parameters across all nodes.
2Measurement precision
If hyperparameter optimization is performed at each local node, then model accuracy improves, but computational complexity and training time increase
Solution Approach 1:
The patent segments the hyperparameter optimization process into local node-level optimizations rather than requiring centralized optimization. Each node independently optimizes its own hyperparameters using its local data, which reduces the computational burden on any single node and allows parallel processing across the network.
Solution Approach 2:
The system performs partial optimization by focusing hyperparameter tuning only on the most critical parameters at each node rather than exhaustively optimizing all parameters. This selective approach reduces training time while still achieving significant accuracy improvements.
3Reliability
If local model parameters are merged to create a global model, then data security is maintained through decentralized architecture, but information loss occurs due to inability to share raw data
Solution Approach 1:
The patent uses merged model parameters as an intermediary that carries information between nodes without requiring direct sharing of raw data. The global model parameters serve as a mediator that encapsulates knowledge from all nodes while preserving data privacy, allowing information exchange through a safe intermediate representation.
Data Source
AI summary
Swarm learning systems and methods can perform hyperparameter tuning. Network nodes train a common machine learning (ML) algorithm using data local to each of the network nodes. Hyperparameter optimization is also performed at each of the network nodes. Model parameters and optimized hyperparameters are shared by the network nodes, and are subsequently merged. An updated version of the common ML algorithm based on the merged model parameters and the merged optimized hyperparameters is generated. That updated version of the common ML algorithm is re-trained at each of the network nodes.