Uplink Scheduling With Adaptive Quantization for Federated Learning
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Solution Overview
Problem
In federated learning systems with a large number of user equipment, communication is slower due to limited resources such as bandwidth and power, leading to inefficiencies in communication resource usage, time consumption, and signaling.
Innovation Solution
A method for resource management that involves determining quantization parameters based on uplink channel information and importance of information for user equipment, allowing for optimized uplink scheduling assignments and message quantization to reduce communication overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is implemented with a large number of UEs, then data privacy protection and heterogeneous data access are improved, but communication speed deteriorates due to limited bandwidth and power resources
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting quantization parameters based on uplink channel conditions and information importance. This allows the system to adapt the precision of model updates to current network conditions, reducing communication overhead while maintaining learning effectiveness, thereby improving communication speed without compromising data privacy protection
2Reliability
If massive number of UEs participate in FL system, then model training comprehensiveness is improved, but communication resource consumption increases
Solution Approach 1:
The patent implements local quality by differentiating the quantization precision for different UEs based on their individual uplink channel conditions and the importance of their model updates. Instead of using uniform quantization for all UEs, the system assigns appropriate quantization parameters to each UE, reducing overall communication resource consumption while maintaining the comprehensiveness of model training across diverse participants
3Measurement precision
If high precision quantization is used for FL messages, then model update accuracy is improved, but communication overhead and time consumption increase
Solution Approach 1:
The patent applies partial action by using differential privacy techniques that add controlled noise to model updates. This approach partially preserves the accuracy of model updates while significantly reducing the precision requirements for communication, thereby decreasing quantization overhead and time consumption without completely sacrificing model update accuracy
4Reliability
If more communication resources are allocated to FL UEs, then communication reliability is improved, but available resources for other services decrease
Solution Approach 1:
The patent implements dynamics by making the quantization parameters dynamic rather than static. The system continuously adapts the quantization precision based on real-time uplink channel conditions and information importance, allowing communication resources to be efficiently utilized with varying precision requirements. This dynamic approach improves communication reliability when needed while preserving resource allocation flexibility for other services
Data Source
AI summary
Embodiments of the present application relate to a method and an apparatus for resource management in wireless communication. According to an embodiment of the present application, a method can include: receiving computing capability information associated with a UE; obtaining a quantization parameter for a message associated with the UE, wherein the quantization parameter is determined based on at least one of: uplink (UL) channel information between the UE and a base station (BS); and importance of information associated with the UE; determining an UL scheduling assignment for the UE based on the computing capability information and the quantization parameter; and transmitting the UL scheduling assignment and the quantization parameter to the UE. Embodiments of the present application can efficiently decrease the communication resource, time consumption, as well as communication signaling in the FL system.