Federated Learning Model Architecture Adaptation
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Solution Overview
Problem
In federated learning, client devices with limited resources often struggle to train computational models within a target training time, leading to bottlenecks and reduced accuracy of centralized models, as existing methods either discriminate against resource-restricted devices or result in partially trained models.
Innovation Solution
An apparatus and method that determine whether a computational model architecture can be trained locally within a target time on client devices, selecting a modified version of the architecture if not, and providing this modified version for local training, ensuring all devices can contribute to the federated learning process without discrimination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a standard computational model architecture is provided to all client devices, then the model accuracy can be maximized, but resource-restricted devices cannot train the model within the target time
Solution Approach 1:
The system provides different computational model architectures tailored to specific client device resource characteristics. Server devices receive the full-accuracy architecture, while resource-restricted client devices receive simplified architectures with reduced complexity (fewer layers, fewer parameters) that can be trained within their specific time and resource constraints.
Solution Approach 2:
The system modifies architectural parameters of the computational model based on client device capabilities. This includes adjusting the number of layers, number of parameters, model width, and depth to match the training capacity of different devices, allowing each device to train effectively within its constraints while still contributing to federated learning.
2Adaptability or versatility
If the computational model architecture is simplified to accommodate resource-restricted devices, then all devices can contribute to federated learning, but the accuracy of the centralized model is reduced
Solution Approach 1:
The system segments the population of client devices into different groups based on their resource characteristics (processing power, memory, training time constraints). Each segment receives an appropriately tailored model architecture from a set of pre-defined or dynamically generated architectures, ensuring that each device can contribute effectively without forcing all devices to use a single simplified model.
3Manufacturing precision
If resource-restricted devices are excluded from federated learning, then model training accuracy is maintained, but the diversity and representativeness of training data is reduced
Solution Approach 1:
The system creates a universal federated learning framework that accommodates multiple device types with different resource capabilities. By providing appropriate model architectures to each device type, the system enables all devices (from high-performance servers to resource-constrained mobile devices) to participate in federated learning, thereby capturing diverse data distributions across different device ecosystems.
Data Source
AI summary
Example embodiments relate to an apparatus, method and computer program relating to federated learning for computational models. In an example, an apparatus comprises means for determining, based on one or more resources of a client device, whether a first computational model architecture can be trained locally by the client device within a target training time. The apparatus may also comprise means for selecting, if the first computational model architecture cannot be trained locally by the client device within the target training time, a modified version of the first computational model architecture that can be trained by the client device within the target training time. The apparatus may also comprise means for providing the selected modified version of the first computational model architecture for local training by the client device.