将异步提升算法融入联邦学习，显著降低通信开销。
Integrating Asynchronous AdaBoost into Federated Learning: Five Real World Applications
- 引入自适应通信调度与延迟权重补偿机制
- 训练时间减少20-35%，通信开销降低30-40%
- 适用于边缘计算、医疗诊断等五类真实场景
本文提出一种增强的异步AdaBoost框架，应用于联邦学习（FL）的五个不同领域：边缘设备上的计算机视觉、基于区块链的模型透明性、移动设备端个性化、物联网异常检测及联邦医疗诊断。该算法通过自适应通信调度和延迟权重补偿，降低同步频率与通信开销，同时保持或提升模型精度。在各场景中评估了训练时间、通信开销、收敛迭代次数和分类准确率等指标。实证结果显示，相较于基线AdaBoost，训练时间减少20-35%，通信开销降低30-40%，收敛所需提升轮次显著减少。表格与图表总结了各领域的改进情况。文中还提供了自适应调度规则与误差驱动同步阈值的数学表达。整体表明，该增强框架在多种联邦学习场景下均展现出更高的效率与鲁棒性，具备广泛适用性。
原文摘要 · Abstract (English)
This paper presents a comprehensive analysis of an enhanced asynchronous AdaBoost framework for federated learning (FL), focusing on its application across five distinct domains: computer vision on edge devices, blockchain-based model transparency, on-device mobile personalization, IoT anomaly detection, and federated healthcare diagnostics. The proposed algorithm incorporates adaptive communication scheduling and delayed weight compensation to reduce synchronization frequency and communication overhead while preserving or improving model accuracy. We examine how these innovations improve communication efficiency, scalability, convergence, and robustness in each domain. Comparative metrics including training time, communication overhead, convergence iterations, and classification accuracy are evaluated using data and estimates derived from Oghlukyan's enhanced AdaBoost framework. Empirical results show, for example, training time reductions on the order of 20-35% and communication overhead reductions of 30-40% compared to baseline AdaBoost, with convergence achieved in significantly fewer boosting rounds. Tables and charts summarize these improvements by domain. Mathematical formulations of the adaptive scheduling rule and error-driven synchronization thresholds are provided. Overall, the enhanced AdaBoost exhibits markedly improved efficiency and robustness across diverse FL scenarios, suggesting broad applicability of the approach.
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