Integrating Asynchronous AdaBoost into Federated Learning: Five Real World Applications

TL;DR

This paper introduces an improved asynchronous AdaBoost algorithm for federated learning, reducing communication costs and enhancing efficiency across five real-world applications without sacrificing accuracy.

Contribution

The paper presents a novel adaptive asynchronous AdaBoost framework with communication scheduling and delay compensation for federated learning, demonstrating broad applicability and improved performance.

Findings

20-35% reduction in training time

30-40% decrease in communication overhead

Fewer boosting rounds for convergence

Abstract

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.