Federated Learning Mode Selection for Limited-Capability User Equipment
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Solution Overview
Problem
Federated learning is often ineffective due to insufficient computing or communication capabilities of user equipment, leading to inefficient model training.
Innovation Solution
An electronic apparatus determines whether user equipment should participate in federated learning independently or dependently based on self-state information, enabling efficient training by splitting models between user equipment and assistant equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If user equipment trains the complete model independently in federated learning, then model training can be performed without cooperation, but the computational burden on user equipment becomes too heavy when computing capability is insufficient
Solution Approach 1:
The patent divides the complete model into multiple sub-models and assigns different sub-models to different user equipment for parallel training. This segmentation reduces the computational burden on each individual device while maintaining overall training effectiveness through coordinated optimization of all sub-models.
2Reliability
If user equipment with insufficient computing capability participates in federated learning independently, then data privacy is maintained, but the federated learning cannot be performed effectively
Solution Approach 1:
By segmenting the model into sub-models, the patent enables user equipment with limited capabilities to participate effectively in federated learning. Each device trains only its assigned sub-model, making the system adaptable to diverse hardware capabilities while preserving data privacy through distributed training.
Solution Approach 2:
The patent assigns different sub-models to different user equipment based on their specific computing capabilities and data characteristics. This local quality approach ensures that each device operates within its capability constraints while contributing to the overall federated learning effectiveness.
3Use of energy by moving object
If the model is split into sub-models for cooperative training, then computational burden is reduced on individual devices, but the system complexity increases
Solution Approach 1:
The patent introduces a server as an intermediary that manages the model splitting, sub-model assignment, and training coordination. This intermediary handles the complex tasks of dividing the model and coordinating the training process, reducing the complexity burden on individual user equipment while enabling efficient distributed training.
Data Source
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AI summary
The present application relates to an electronic device and a method for wireless communication, and a computer-readable storage medium. The electronic device for wireless communication comprises a processing circuit, wherein the processing circuit is configured to determine, according to state information reported by a user equipment within a service range of the electronic device, whether the user equipment is to cooperate with other user equipments to train a segmented model obtained by segmenting a model to be trained, so as to participate in federated learning in a non-independent way, or to independently train the model to be trained, so as to independently participate in federated learning. (FIG. 1)