OPTIMIZING INTELLIGENT EDGE COMPUTING RESOURCE SCHEDULING BASED ON FEDERATED LEARNING

Abstract

This study proposes a novel federated learning framework for optimizing intelligent edge computing resource scheduling. The framework addresses the challenges of device heterogeneity, non-IID data distribution, and communication overhead in edge environments. We introduce an adaptive client selection mechanism considering computational capabilities, energy status, and data quality. A personalized model training approach is implemented to handle non-IID data effectively using multi-task learning and local batch normalization layers. The framework incorporates efficient model aggregation techniques and communication-efficient updates to reduce bandwidth consumption. The privacy policy, including the difference between privacy and collective security, has been integrated to improve data protection. We develop scheduling problems based on multi-objective optimization, combining the best in computing and communication while updating local and global guidelines. Extensive testing on a wide range of data shows that the framework is superior regarding connection speed, resource utilization, and model performance. The proposed method achieves a 15% improvement in model accuracy and a 40% reduction in communication overhead compared to learning state-of-the-art algorithms. Case studies in intelligent city traffic prediction and healthcare IoT validate the framework's effectiveness in real-world scenarios, showcasing its scalability and adaptability to varying network conditions and client availability.
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本研究提出了一种新的联邦学习框架，用于优化智能边缘计算资源调度。该框架解决了边缘环境中的设备异构性、非 IID 数据分发和通信开销等挑战。我们引入了一种考虑计算能力、能源状态和数据质量的自适应客户端选择机制。实施个性化模型训练方法，以使用多任务学习和本地批量归一化层有效地处理非 IID 数据。该框架结合了高效的模型聚合技术和通信高效的更新，以减少带宽消耗。已整合隐私政策，包括隐私和集体安全之间的区别，以改进数据保护。我们基于多目标优化开发调度问题，结合计算和通信的优点，同时更新本地和全局指南。对大量数据的广泛测试表明，该框架在连接速度、资源利用率和模型性能方面具有优势。与学习最先进的算法相比，所提出的方法使模型准确性提高了 15%，通信开销减少了 40%。智能城市交通预测和医疗保健 IoT 的案例研究验证了该框架在实际场景中的有效性，展示了其对不同网络条件和客户端可用性的可扩展性和适应性。