Sidelink Federated Learning for Wireless Model Training
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Solution Overview
Problem
Current wireless communication systems face challenges in efficiently training machine learning components using federated learning due to limitations in processing capacity, memory, and power at user equipment (UEs), which can lead to inefficiencies in updating machine learning models.
Innovation Solution
The implementation of sidelink-supported federated learning, where UEs can transfer subsets of local training data to each other for combined updates, offloading processing from UEs with limited capabilities while maintaining data privacy through base station approval and encryption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is implemented at user equipment, then machine learning model training can be distributed and data privacy can be maintained, but processing capacity, memory, and power constraints at UEs lead to training inefficiency
Solution Approach 1:
The patent introduces a base station as an intermediary that coordinates federated learning operations between UEs. The base station receives training data from multiple UEs, performs centralized model training, and distributes updated models back to UEs, thereby maintaining data privacy while overcoming individual UE resource limitations through coordinated network-based processing
Solution Approach 2:
The patent combines training data from multiple UEs at the base station to perform centralized model training. By merging resources and computing power across multiple devices through the network, the system achieves training efficiency that exceeds what any single UE could accomplish independently, while still maintaining the distributed privacy-preserving architecture
2Adaptability or versatility
If UEs with limited capabilities perform local training, then distributed learning can be maintained, but the burden on resource-constrained UEs reduces overall system performance
Solution Approach 1:
The patent allows UEs with limited capabilities to participate in federated learning by contributing their local training data to the base station, rather than requiring them to perform complete local training cycles. This partial participation approach enables resource-constrained devices to contribute to distributed learning without being overwhelmed by computational burdens, thereby maintaining system adaptability while improving overall performance
3Productivity
If more processing is done at UEs to improve training efficiency, then model updates can be faster, but power consumption and device resources are exceeded
Solution Approach 1:
The base station acts as a mediator that performs the computationally intensive model training operations centrally, allowing UEs to achieve fast model updates without performing heavy local processing. UEs simply transmit their training data and receive updated models from the base station, thereby achieving rapid model convergence while keeping power consumption at UE levels manageable
Data Source
AI summary
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a first user equipment (UE) may receive, from a second UE, a sidelink communication that indicates a first subset of local training data of a set of local training data associated with the second UE. The UE may transmit a combined local update that is based at least in part on training a machine learning component based at least in part on the first subset of local training data and a second set of local training data associated with the first UE. Numerous other aspects are described.