Dynamic Processing Node Selection in Federated Learning
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Solution Overview
Problem
Current federated learning architectures have a fixed processing node for generating common models, which may not be optimal in scenarios where network topology changes or data generation patterns change.
Innovation Solution
A method and apparatus for dynamically determining a processing node for each round of model processing based on actual requirements, allowing adaptation to changes in application scenarios, network topology, and data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed central server is used as the processing node for generating common models, then the system structure is simple and easy to implement, but the system cannot adapt to changes in network topology structure and data generation patterns
Solution Approach 1:
The patent implements dynamic processing node selection where the processing node for each round of model processing is determined based on actual requirements rather than being fixed. This allows the system to adapt to changes in network topology and data generation patterns by dynamically selecting the most suitable processing node for each round, resolving the contradiction between adaptability and system simplicity.
2Reliability
If the common model is transmitted to all participating nodes after each round of processing, then all nodes can update their models, but the communication overhead increases significantly
Solution Approach 1:
The patent extracts the common model only to the specific processing node for the next round of processing rather than distributing it to all participating nodes. This selective transmission approach maintains model update reliability while significantly reducing communication overhead by transmitting the model only where it is needed for the next processing round.
3Adaptability or versatility
If multiple processing nodes are involved in model processing, then the system flexibility and adaptability improve, but the coordination and management complexity increases
Solution Approach 1:
The patent determines the processing node for the next round of processing in advance, before the actual processing occurs. This preliminary determination simplifies coordination by pre-establishing which node will handle the next round, reducing the complexity of real-time node management while maintaining the flexibility to use multiple processing nodes throughout the training process.
Data Source
AI summary
This application provides a model training method and apparatus. The method includes: A first processing node obtains at least one first model; the first processing node processes the at least one first model to generate a first common model; and the first processing node determines a second processing node, where the second processing node is a processing node for a next round of model processing, and the first common model is obtained by the second processing node before the next round of model processing. In technical solutions provided in this application, before the next round of model processing, a processing node for the next round of model processing may be determined based on an actual requirement, to adapt to a change of an application scenario.