Federated Neural Network Parameter Selection
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Solution Overview
Problem
Federated learning of neural networks faces challenges in efficiently communicating and aggregating parameter updates across multiple client nodes due to data protection regulations and high bandwidth requirements, where small changes in parameters can be lost or cancel each other out, leading to inefficient data transmission.
Innovation Solution
Selecting relevant parameters based on a predefined criterion, such as the Fisher information matrix, to determine the most impactful changes, and aggregating these changes using methods like averaging or offsetting gradients to optimize the neural network parameters, reducing unnecessary data transmission and improving training efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all parameters are transmitted to the server node for federated training, then training accuracy is improved, but communication bandwidth requirement increases significantly
Solution Approach 1:
The patent extracts only the most relevant parameters from the complete parameter set and transmits only these selected parameters to the server node. The selection is based on a predefined criterion that identifies parameters with the highest relevance to the training task, thereby reducing communication bandwidth while maintaining training accuracy.
Solution Approach 2:
The parameter space is segmented into relevant and irrelevant parameters. By dividing the complete parameter set into meaningful subsets based on relevance criteria, the system transmits only the necessary segment of parameters, reducing the overall communication volume while preserving the essential information for accurate training.
2Reliability
If all client nodes transmit proposed changes for all parameters, then training completeness is improved, but transmission time increases
Solution Approach 1:
The system extracts and transmits only the proposed changes for relevant parameters rather than all parameters. This selective extraction maintains training completeness for the most impactful parameters while significantly reducing transmission time by excluding irrelevant parameter updates.
Solution Approach 2:
Instead of requiring complete transmission of all parameter changes from all client nodes, the system applies partial action by transmitting only the necessary parameter updates. This partial approach suffices for achieving effective training while reducing transmission time and computational overhead.
3Productivity
If small proposed changes in parameters are transmitted, then communication efficiency is improved, but these changes may be lost or cancel out
Solution Approach 1:
The system extracts and transmits only proposed changes that meet the predefined relevance criterion. By filtering out changes that are too small or irrelevant, the system maintains communication efficiency while preventing loss of meaningful information through selective transmission of only significant parameter updates.
Solution Approach 2:
The system dynamically adjusts which parameters are transmitted based on the magnitude and relevance of proposed changes. Parameter changes are selectively transmitted or aggregated based on their significance, ensuring that small but meaningful changes are preserved while filtering out negligible variations that would cancel out or waste bandwidth.
Data Source
AI summary
A method for generating a training contribution for a neural network on a client node for a federated training of the neural network. In the method, a complete set of parameters characterizing the behavior of the neural network is received; the parameterized neural network is supplied with training examples from a predefined set so that the neural network in each case delivers outputs, wherein the training examples are labeled with target outputs; deviations of the outputs from the respective target outputs are evaluated with a predefined cost function; the parameters of the neural network are optimized with the aim of improving the evaluation by the cost function; a set of particularly relevant parameters is selected based on a predefined criterion; for the selected parameters, proposed changes are ascertained as the sought training contribution based on the result of the optimization; the proposed changes are transmitted to a server node.