Federated Learning Uplink Coding Based on Gradient Norm
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Solution Overview
Problem
The efficiency of wireless federated learning is reduced due to suboptimal modulation and coding schemes in existing wireless communication systems, leading to increased training delays and bit errors in the transmission of local training results.
Innovation Solution
Determine modulation and coding schemes based on the norm of the gradient of the learning model, considering factors like bit error rate and computing capability to optimize training efficiency and reduce signaling overheads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If modulation and coding schemes are selected based on channel state in existing wireless communication systems, then communication reliability is maintained, but training efficiency of global learning model deteriorates due to suboptimal scheme selection
Solution Approach 1:
The patent changes the basis for selecting modulation and coding schemes from channel state parameters to gradient norm parameters. The gradient norm reflects the importance of local training results, allowing the system to dynamically adjust transmission parameters based on the significance of the data being transmitted rather than just channel conditions, thereby improving training efficiency while maintaining reliability
Solution Approach 2:
The system implements dynamic selection of modulation and coding schemes by introducing gradient norm as a time-varying parameter that changes with each federated learning round. This dynamic approach allows the transmission scheme to adapt to the changing importance of local training results, optimizing the balance between reliability and training efficiency throughout the learning process
2Productivity
If optimal modulation and coding schemes are selected based on gradient norm, then training efficiency is improved, but system complexity increases due to additional norm calculation and scheme determination
Solution Approach 1:
The gradient norm is calculated in advance during the local training process before transmission begins. This preliminary calculation allows the system to determine the appropriate modulation and coding scheme ahead of time, avoiding complex real-time decisions during transmission and reducing overall system complexity while maintaining improved training efficiency
3Ease of manufacture
If traditional channel-state-based modulation and coding scheme selection is used, then implementation simplicity is maintained, but bit errors increase due to suboptimal scheme matching
Solution Approach 1:
The patent introduces gradient norm as an intermediary parameter that bridges the gap between local training results and transmission parameter selection. This intermediary provides a meaningful metric that directly reflects the importance of transmitted data, enabling more accurate modulation and coding scheme selection that reduces bit errors while maintaining implementation feasibility through straightforward norm calculation
Data Source
AI summary
A communication method, apparatus, and computer-readable storage medium are provided to improve the efficiency of federated learning by determining a modulation and coding scheme that maximizes training efficiency of a second learning model. The modulation and coding scheme is used for processing information about a first learning model of a first communication apparatus and the information about the first learning model is used for determining the second learning model, where the second learning model is configured to update the first learning model of the first communication apparatus. The modulation and coding scheme is determined based on a first norm where the first norm is a norm of a gradient of the first learning model of the first communication apparatus. The first norm of the first communication apparatus represents an importance degree of a local dataset of the first communication apparatus.