Training Task Allocation in Federated Edge Learning: A Matching-Theoretic Approach

Abstract

Federated edge learning has emerged as a promising technique to enable distributed machine learning using local datasets from large-scale edge devices, e.g., mobile phones or parked vehicles, that share only model updates without uploading raw training data. This technique not only preserves data privacy of edge devices but also simultaneously ensures high learning performance. However, the emerging federated edge learning still confronts serious challenges, such as the lack of efficient training task assignment schemes with reliable edge devices acting as workers. To address this challenge, we utilize a many-to-one matching model to solve the training task assignment problem between the workers and multiple task publishers. In the matching model, we minimize not only the overall training time of the task publishers but also the energy consumption of the workers. To define against malicious model updates from unreliable workers, we present reputation as a metric to evaluate the reliability and trustworthiness of the edge devices, and also take the reputation into consideration when assigning training tasks. The numerical results indicate that the proposed schemes can efficiently improve the performance of federated edge learning.