Federated Learning Controller Segmentation for Heterogeneous Data
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Solution Overview
Problem
Federated learning systems face challenges in utilizing heterogeneous training data across different nodes, often excluding unique features and information, which limits the performance of global models and requires cumbersome personalization processes.
Innovation Solution
A customizable federated learning approach that identifies common and subset-specific datasets within the system, allowing nodes to train bifurcated models using a controller that configures separate architectures for shared and unique data, enabling the aggregation of models while accounting for local data features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If heterogeneous data is excluded from training to maintain system simplicity, then device complexity is reduced, but information loss increases as unique local features are discarded
Solution Approach 1:
The patent segments the training data into two distinct datasets: a first dataset containing features common to all nodes, and a second dataset containing features unique to subset nodes. This segmentation allows each dataset to be processed appropriately - common features contribute to the global model while unique features are preserved for local customization, thereby reducing information loss without overwhelming system complexity
Solution Approach 2:
The patent applies local quality by training a first model using common features for the global model, and a second model using unique local features for local customization. This allows each part of the system to utilize the appropriate data quality - global nodes benefit from common features while subset nodes leverage their unique local features, maximizing information utilization without uniform complexity across all nodes
2Adaptability or versatility
If separate models are trained for common and unique data features, then adaptability improves by incorporating local features, but device complexity increases due to multiple model training processes
Solution Approach 1:
The patent segments model training into two distinct processes: training a first model on common features for global aggregation, and training a second model on unique local features for local customization. This segmentation enables the system to achieve high adaptability by incorporating both common and unique features, while managing complexity through structured separation of training processes
Solution Approach 2:
The patent implements local quality by applying different training approaches to different data types - the first model uses common features suitable for all nodes, while the second model uses unique local features tailored to subset nodes. This allows the global model to be adaptable and personalized for different locations, while the training complexity is managed through this quality-based differentiation
3Productivity
If only common data features are used for global model training, then device complexity is reduced, but productivity decreases as unique local features remain underutilized
Solution Approach 1:
The patent segments the utilization of training data into two pathways: common features are used to train the first model for global aggregation, while unique local features are used to train the second model for local customization. This segmentation increases productivity by fully utilizing all available training data, rather than discarding unique local features, while managing complexity through structured data processing
Data Source
AI summary
In one embodiment, a controller for a federated learning system identifies a first dataset and a second dataset available to a particular node of the federated learning system. The first dataset comprises features that are common to all nodes of the federated learning system. The second dataset comprises features that are common only to a subset of nodes of the federated learning system. The controller configures the particular node to train a first model using the first dataset. The controller causes formation of a global model in the federated learning system that aggregates the first model from the particular node with models from all other nodes of the federated learning system. The controller configures the particular node to train a second model using the second dataset.