Device Selection and Resource Allocation with Semi-supervised Method for Federated Edge Learning

With the rapid growth of distributed learning and workflow orchestration, Federated Edge Learning has emerged as a solution, enabling multiple edge devices to collaboratively train a large model without the need for sharing raw data. Beyond considering bandwidth and computational resource limitations in the Internet of Things (IoT) environment, it is crucial to address the issue of IoT devices often collecting data that lacks timely annotations, which can lead to latency and label deficiency issues. In most Federated Edge Learning mechanisms, clients' weights are selected for offloading to the server. In this paper, we propose a solution for dynamic edge selection and wireless network allocation under semi-supervised and privacy protection settings, termed Semi-supervised Scheduling and Allocation Optimization for Federated Edge Learning (SSAFL). SSAFL is designed to adapt to various scenarios, including channel state variations, device heterogeneity, resource incentives, deadline control, label deficiencies, and Non-IID data distributions. This adaptability is achieved through the utilization of an Incentive Optimization framework that encompasses bandwidth allocation and device scheduling policies. Within SSAFL, we introduce the concept of a weighted bipartite graph network to tackle the Incentive Optimization problem and achieve a balance in large-scale optimization of device selection. Additionally, to address the label deficiency issue, we devise a Dynamic Timer for deadline control for each client. Comprehensive and confidential results demonstrate that our proposed approach significantly outperforms other Federated Edge Learning baselines.