Partially Trainable Networks for Federated Learning Bandwidth Reduction
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Solution Overview
Problem
Federated learning systems face challenges due to limited communication bandwidth and local computation resources on mobile devices, hindering scalability and usability, particularly in cross-device settings where training large models is inefficient and resource-intensive.
Innovation Solution
The implementation of partially trainable neural networks (PTNs) where a subset of parameters are frozen and reconstructed using an initialization value, reducing communication costs and computational burdens by only transmitting and updating trainable parameters, while maintaining model performance through federated averaging and differential privacy techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all parameters of the global model are transmitted to client devices for training, then model performance can be maintained, but communication bandwidth requirements and computational burdens increase significantly
Solution Approach 1:
The patent segments the global model parameters into two distinct sets: trainable parameters and frozen parameters. The server determines which parameters should be trainable based on the model architecture and training requirements. By dividing the parameter space, the system transmits only the trainable parameters to client devices, significantly reducing communication bandwidth consumption while maintaining model performance through selective training of critical parameters.
Solution Approach 2:
The patent extracts and identifies the essential trainable parameters from the complete set of model parameters using parameter importance analysis. By taking out only the most critical parameters that contribute to model performance and freezing the less important ones, the system reduces the quantity of data transmitted to clients while preserving the core learning capability of the model.
2Productivity
If all parameters are updated and transmitted in each federated learning round, then model convergence is faster, but computational resources and energy consumption on client devices increase
Solution Approach 1:
The patent segments the parameter update process into selective updates of only trainable parameters on client devices, while frozen parameters remain unchanged. This segmentation allows clients to perform computations on a reduced subset of parameters, significantly lowering energy consumption and computational resource usage while maintaining effective model convergence through coordinated updates of the critical trainable parameters.
Solution Approach 2:
The patent applies partial action by updating only the necessary trainable parameters rather than all parameters in each federated learning round. This partial update strategy reduces the computational burden and energy consumption on client devices while still achieving effective model convergence by focusing computational resources on the parameters that most influence model performance.
3Measurement precision
If the complete model is transmitted to each client device, then local training accuracy is improved, but device memory requirements and system complexity increase
Solution Approach 1:
The patent segments the model parameters transmitted to client devices into only the trainable subset, excluding frozen parameters. This segmentation reduces the memory requirements on client devices by a significant margin while maintaining local training accuracy, as the trainable parameters contain the essential information needed for effective local learning without requiring the complete model architecture to be stored locally.
Data Source
AI summary
Example aspects of the present disclosure provide a novel, resource-efficient approach for federated machine learning techniques with PTNs. The system can determine a first set of training parameters from a plurality of parameters of the global model. Additionally, the system can generate a random seed, using a random number generator, based on a set of frozen parameters. Moreover, the system can transmit, respectively to a plurality of client computing devices, a first set of training parameters and the random seed. Furthermore, the system can receive, respectively from the plurality of client computing devices, updates to one or more parameters in the first set of training parameters. Subsequently, the system can aggregate the updates to one or more parameters that are respectively received from the plurality of client computing devices. The system can modify one or more global parameters of the global model based on the aggregation.