Integrating Asynchronous AdaBoost into Federated Learning: Five Real World Applications
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionFederated LearningSchedulingSimilar Papers 제목 키워드 기반
LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data
Intensive care data are valuable for improvement of health care, policy making and many other purposes. Vast amount of such data are stored in different locations, on many different devices and in different data silos. S…
BIG-bench Machine LearningEmpirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs
Device heterogeneity poses major challenges in Federated Learning (FL), where resource-constrained clients slow down synchronous schemes that wait for all updates before aggregation. Asynchronous FL addresses this by inc…
Emotion RecognitionFairnessFederated LearningSpeech Emotion RecognitionOrthogonal Calibration for Asynchronous Federated Learning
Asynchronous federated learning mitigates the inefficiency of conventional synchronous aggregation by integrating updates as they arrive and adjusting their influence based on staleness. Due to asynchrony and data hetero…
Federated Learningglobal-optimizationFADAS: Towards Federated Adaptive Asynchronous Optimization
Federated learning (FL) has emerged as a widely adopted training paradigm for privacy-preserving machine learning. While the SGD-based FL algorithms have demonstrated considerable success in the past, there is a growing …
Federated LearningPrivacy PreservingFederated Learning based on Pruning and Recovery
A novel federated learning training framework for heterogeneous environments is presented, taking into account the diverse network speeds of clients in realistic settings. This framework integrates asynchronous learning …
Federated Learning