<ul data-eligibleForWebStory="true"><li>This paper introduces an enhanced asynchronous AdaBoost framework for federated learning (FL) with applications in computer vision, blockchain, mobile personalization, IoT anomaly detection, and healthcare diagnostics.</li><li>The algorithm incorporates adaptive communication scheduling and delayed weight compensation to reduce synchronization frequency and communication overhead while maintaining or enhancing model accuracy.</li><li>The study evaluates improvements in communication efficiency, scalability, convergence, and robustness in each domain through comparative metrics such as training time, communication overhead, convergence iterations, and classification accuracy.</li><li>Empirical results demonstrate notable reductions in training time (20-35%) and communication overhead (30-40%) compared to baseline AdaBoost, with faster convergence in boosting rounds.</li><li>The research provides mathematical formulations for adaptive scheduling and error-driven synchronization thresholds, illustrating enhanced efficiency and robustness in various FL scenarios.</li></ul>

Integrating Asynchronous AdaBoost into Federated Learning: Five Real World Applications

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