Federated Learning Update Selection to Reduce Signaling Overhead
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Solution Overview
Problem
Federated learning in deep neural networks incurs significant signaling overhead due to the transmission of parameter updates across multiple nodes, particularly in wireless networks, which consumes time, frequency, and computing resources.
Innovation Solution
Implement a method to evaluate the training performance of machine-learning models at local nodes, determining which updates to transmit based on metrics such as fitting status and update characteristics, thereby reducing unnecessary signaling by only transmitting updates that significantly improve model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all parameter updates are transmitted in each training round, then the global model can be updated comprehensively, but the signaling overhead increases significantly
Solution Approach 1:
The patent extracts only the essential parameter updates that meet predefined transmission criteria from the complete set of updates. Local nodes evaluate their training results and selectively transmit only those parameter updates that satisfy specific conditions (e.g., gradient norm threshold, sparsity threshold, or convergence criteria), thereby removing unnecessary transmission data while preserving model update effectiveness.
Solution Approach 2:
The patent implements partial action by transmitting a subset of parameter updates rather than all updates. By applying transmission criteria that filter updates based on their significance or contribution to model convergence, the system performs a partial transmission that is sufficient for model improvement while avoiding the excessive signaling overhead of transmitting all parameters.
2Quantity of substance
If parameter updates are selectively transmitted based on training evaluation, then signaling overhead is reduced, but the complexity of determining which updates to transmit increases
Solution Approach 1:
The patent changes the parameters of the transmission decision by introducing multiple evaluable criteria (gradient norm, sparsity, convergence metrics) that can be computed locally. These parameter-based criteria transform the complex selection problem into a series of straightforward threshold comparisons and metric calculations, making the selection process more systematic and less complex.
Solution Approach 2:
The patent enables local nodes to autonomously evaluate their own training results and determine which updates to transmit based on predefined criteria. This self-service mechanism eliminates the need for centralized selection logic, distributing the decision-making process to where the training occurs and reducing overall system complexity while maintaining effective update selection.
3Quantity of substance
If transmission criteria are applied to filter updates, then unnecessary transmissions are reduced, but the time required to evaluate and select updates increases
Solution Approach 1:
The patent applies preliminary action by pre-defining transmission criteria and thresholds before the federated learning process begins. Local nodes use these predetermined criteria to quickly evaluate their updates without needing to perform complex real-time analysis, significantly reducing the evaluation time during each training round while maintaining effective filtering of unnecessary transmissions.
Data Source
AI summary
According to the present disclosure, it may be determined whether or not an update to a parameter of a machine-learning model is to be transmitted based on an evaluation of the training of the machine-learning model. The transmission of updates to a machine-learning model may be based on an evaluation of the training of the machine-learning model.