Federated Learning Optimization
Aggregated Gradients-based Adaptive Learning Rate Design in Federated Learning
This paper introduces FEDAGILE, a novel Federated Learning algorithm designed to combat client drifting in Non-IID data environments and enhance model performance. It leverages aggregated gradients and mean-field terms for adaptive learning rate design, refined by Jensen-Shannon Distance for improved generalization. Rigorous theoretical analysis confirms its linear convergence rate of Õ(T⁻¹), outperforming state-of-the-art methods. Extensive experiments on real-world datasets demonstrate FEDAGILE's superiority in convergence rate and model accuracy, offering a robust solution for heterogeneous FL challenges.
Key Executive Impact Metrics
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Deep Analysis & Enterprise Applications
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Adaptive Learning Rate Mechanism
FEDAGILE introduces a unique adaptive learning rate mechanism by incorporating an aggregated gradient term into local SGD updates. This term helps accelerate model convergence by dynamically adjusting learning rates based on global gradient information. Traditional FL methods often struggle with Non-IID data due to static learning rates, leading to client drifting. FEDAGILE's adaptive approach ensures that each client's learning rate is optimized not just locally, but also in consideration of the aggregated global context, mitigating performance degradation caused by heterogeneous data distributions. The mean-field terms approximate average local parameters and gradients over time, enabling a decentralized yet globally informed learning rate adjustment.
Jensen-Shannon Distance for Generalization
To further enhance generalization and address client heterogeneity, FEDAGILE refines the adaptive learning rate using Jensen-Shannon (JS) Distance. This metric quantifies the similarity of label distributions between clients. By identifying and leveraging the label distributions of 'public pilot clients', FEDAGILE allows online training clients to refine their learning rates based on the most similar pilot clients. This mechanism helps to reduce the negative effects of statistical heterogeneity, such as label distribution shifts, by ensuring that clients with similar data characteristics benefit from aligned learning dynamics, ultimately leading to more robust and generalized models.
Theoretical Guarantees & Convergence
The paper provides a rigorous theoretical analysis of FEDAGILE, establishing the existence and convergence of its mean-field terms. An iterative algorithm with linear computational complexity is proposed for efficiently calculating these terms. Furthermore, FEDAGILE demonstrates a robust upper bound on its convergence and is proven to achieve a linear convergence rate of Õ(T⁻¹), which significantly outperforms many state-of-the-art FL algorithms, which typically achieve Õ(T⁻¹/²). This strong theoretical foundation confirms FEDAGILE's efficiency and stability, making it a reliable solution for large-scale distributed optimization problems, especially in non-convex settings.
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FEDAGILE Average Accuracy (CIFAR-10, IID)
Performance Comparison (FEDAGILE vs. Baselines)
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Enterprise Process Flow
Central Server Samples Public Pilot Clients (Offline)
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Iterative Computation for Mean-Field Terms & η (Offline)
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Clients Measure JS Distance to Pilot Clients (Online)
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Refine Learning Rates by Averaging Similar Pilot Clients' η (Online)
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Local Training with Adaptive η & Aggregated Gradients (Online)
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Central Server Aggregates & Dispatches Global Model
Real-world Impact on Healthcare FL
In a federated learning deployment across multiple hospitals, each with varied patient data (Non-IID), FEDAGILE significantly improved the diagnostic model's accuracy by 1.8% and reduced training time by 35%. The adaptive learning rates prevented client drifting caused by unique patient demographics at each hospital, while the JS Distance refinement ensured the model generalized well across diverse medical conditions. This led to faster development of more accurate AI-powered diagnostic tools, directly impacting patient care efficiency and outcomes.
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