Federated Learning Model Splitting for Lower Wireless Update Overhead
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently managing federated learning across multiple user equipment (UEs) due to the complexity and overhead associated with model updates and parameter exchanges.
Innovation Solution
A network node receives parameter updates from multiple UEs and transmits combined models based on these updates, optimizing the federated learning process by reducing redundant transmissions and enhancing model efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If federated learning model updates are transmitted to multiple UEs, then model training accuracy is improved, but network overhead and transmission complexity increase
Solution Approach 1:
The model is segmented into multiple versions and selectively transmitted to different UEs based on their capabilities and training needs. Instead of transmitting the complete model to all UEs, the network node divides the model into multiple versions (e.g., full model, partial model, or model variants) and distributes them selectively, reducing overall network overhead while maintaining training accuracy for each UE.
Solution Approach 2:
The network node transmits only the necessary portion of the model to each UE rather than the complete model. Some UEs receive partial model updates or only specific model components they need for their local training, avoiding redundant transmissions of unnecessary model parameters and reducing network complexity.
2Stability of the object's composition
If complete model updates are transmitted to all UEs, then model consistency is improved, but transmission time and network resource consumption increase
Solution Approach 1:
Different UEs receive different model versions tailored to their specific local conditions, capabilities, and training requirements. The network node assigns model versions locally to each UE based on their characteristics, allowing faster transmission times while maintaining the appropriate level of model consistency needed for each device's effective training.
Solution Approach 2:
The network node transmits partial model updates or selective model components to UEs rather than complete model updates to all devices. This reduces transmission time and network resource consumption while maintaining sufficient model consistency through selective updates and centralized coordination of model versions.
3Productivity
If multiple model versions are managed for different UEs, then model efficiency for specific devices is improved, but system complexity increases
Solution Approach 1:
The network node dynamically manages multiple model versions and selectively assigns them to different UEs based on real-time conditions, device capabilities, and training performance. This dynamic allocation improves model efficiency for each device while the centralized management at the network node keeps system complexity manageable through automated version control and assignment.
Solution Approach 2:
The network node acts as an intermediary that manages the complexity of multiple model versions centrally, while individual UEs interact with simplified single model versions. This intermediary approach allows multiple model versions to be maintained efficiently at the network level without increasing complexity at the UE level, improving overall system efficiency.
Data Source
AI summary
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a network node may receive, from a first user equipment (UE) and a second UE, one or more parameter updates associated with a first model for federated learning. The network node may transmit, to the first UE, a second model for federated learning that is based at least in part on the first model. The network node may transmit, to the second UE, a third model for federated learning that is based at least in part on the first model. Numerous other aspects are described.