Federated Learning Reporting Criteria for Wireless Training Efficiency
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Solution Overview
Problem
Existing federated learning models require centralized data collection and training, which leads to inefficient data exchange and increased computational burden, especially in wireless communication systems.
Innovation Solution
An apparatus and method for determining reporting criteria at distributed nodes to selectively provide reports to a central node, allowing for optimized participation in federated learning processes based on contextual and resource availability, thereby reducing unnecessary data exchange.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If centralized data collection and training is used, then comprehensive model training can be achieved, but data exchange efficiency decreases and computational burden increases
Solution Approach 1:
The patent segments the centralized training process into distributed training tasks across multiple nodes. Each node performs local training independently and reports only when specific criteria are met, dividing the overall computational burden while maintaining training effectiveness through selective reporting to the central node.
Solution Approach 2:
The system implements partial action by having nodes report only when reporting criteria are satisfied, rather than continuously reporting all data. This selective reporting reduces unnecessary data exchange and computational overhead while still providing sufficient information for effective model training.
2Loss of information
If continuous reporting is performed, then comprehensive feedback is provided to the central node, but data transmission overhead increases
Solution Approach 1:
The system applies partial action by implementing conditional reporting where nodes only transmit data when reporting criteria are satisfied. This approach provides sufficient feedback information to the central node while minimizing unnecessary data transmission overhead.
Solution Approach 2:
The patent changes the reporting parameter from continuous to event-driven based on criterion satisfaction. This parameter change transforms the reporting mechanism into a more efficient system that adapts transmission volume based on actual need, reducing overall data transmission while maintaining feedback quality.
3Adaptability or versatility
If all nodes participate in training, then model diversity is maximized, but resource utilization becomes inefficient
Solution Approach 1:
The patent applies local quality by allowing different nodes to have different participation states in the training process. Nodes that meet reporting criteria actively participate and provide diverse feedback, while nodes that don't meet criteria remain inactive, optimizing resource utilization while maintaining model diversity through selective participation.
Solution Approach 2:
The system implements partial participation where only nodes satisfying specific criteria engage in training activities. This partial action approach ensures that active participants have sufficient resources while maintaining diversity through multiple potential participants, avoiding the inefficiency of forcing all nodes to participate regardless of resource availability.
Data Source
AI summary
The disclosure relates to an apparatus comprising at least one processor and at least one memory including computer code for one or more programs, the at least one memory and the computer code configured, with the at least one processor, to cause the apparatus at least to: determine (700) whether at least one reporting criterion is met; and trigger (702) the provision of a report to a central node when the at least one reporting criterion is met, wherein the report comprises an indication that at least one reporting criterion is met and/or an indication of the at least one reporting criterion being met.