Adaptive Federated Learning in Resource Constrained Edge Computing Systems

Abstract: Emerging technologies and applications including Internet of Things (IoT), social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place. Our focus is on a generic class of machine learning models that are trained using gradient-descent based approaches. We analyze the convergence bound of distributed gradient descent from a theoretical point of view, based on which we propose a control algorithm that determines the best trade-off between local update and global parameter aggregation to minimize the loss function under a given resource budget. The performance of the proposed algorithm is evaluated via extensive experiments with real datasets, both on a networked prototype system and in a larger-scale simulated environment. The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions.

• The paper introduces an adaptive algorithm that controls global aggregation intervals to optimize resource usage in resource-constrained edge computing environments.
• It derives a convergence bound for gradient-descent-based federated learning, addressing challenges from non-i.i.d. data distributions across nodes.
• Experimental results show that the adaptive method outperforms fixed-interval strategies across diverse models and data distributions, enhancing efficiency and robustness.

Adaptive Federated Learning in Resource Constrained Edge Computing Systems

The paper addresses the problem of federated learning in resource-constrained edge computing environments, particularly focusing on gradient-descent-based training algorithms. The rise of data-intensive applications like the Internet of Things (IoT) and social networking has led to a deluge of data generated at the network edge. Traditional approaches that rely on centralized data collection confront significant hurdles due to bandwidth limitations, storage constraints, and privacy considerations. Federated learning (FL), where raw data remains localized and only model parameters are exchanged, emerges as a practical solution.

Theoretical Contributions and Algorithmic Design

Central to the paper is an algorithm that dynamically adjusts the intervals between global aggregation steps during the federated learning process to optimize resource usage. The learning workflow involves alternating between local updates at edge nodes and global aggregations. The authors propose an adaptive control mechanism to determine the most efficient trade-off between the frequency of local updates and global aggregation.

Key Theoretical Contributions:

1. Convergence Analysis: Using theoretical analysis, the paper derives a convergence bound for gradient-descent-based FL, encompassing arbitrary numbers of local updates between global aggregations and accounting for non-i.i.d. data distributions across nodes.
2. Algorithm Development: On the basis of the derived convergence bounds, the authors develop an adaptive control algorithm. This algorithm dynamically adjusts the frequency of global aggregations to minimize the loss function while adhering to a fixed resource budget.

Algorithm Implementation:

• The proposed control algorithm estimates resource consumption (such as time and energy) and model characteristics (such as smoothness and gradient divergence) in real time.
• It employs these estimates to recompute the optimal frequency of global aggregations ($\tau$) at each aggregation step.
• The experimentation shows near-optimal performance for various data distributions, model types, and system configurations both in real-world environments (with prototypes) and simulated settings.

Experimentation and Performance Evaluation

The empirical evaluation utilizes multiple machine learning models—squared-SVM, linear regression, K-means, and deep convolutional neural networks (CNN). Diverse datasets, like MNIST for classification tasks and an energy consumption dataset for regression, were employed. The paper examines four cases of data distribution:

• Case 1: Uniform distribution of data samples across nodes.
• Case 2: Each node contains only one class of data.
• Case 3: All nodes have identical datasets.
• Case 4: A combination of Cases 1 and 2, where the first half of the nodes have uniformly distributed data, and the second half have class-specific data.

Each experimental setup aims to assess not only the efficacy of the proposed adaptive algorithm but also the pertinence of traditional non-adaptive FL approaches.

Results:

• The proposed approach dynamically adjusts $\tau$ to effectively balance resource consumption and model convergence.
• It outperforms non-adaptive approaches (fixed $\tau$) across different distributions and model types.
• The algorithm adapts to varying resource constraints, showing its robustness and flexibility in real-world deployments.

Practical and Theoretical Implications

Theoretical implications of this research extend to the analysis of federated learning convergence properties, introducing bounds that provide insight into how aggregation frequencies influence model training efficacy. Practically, the adaptive algorithm offers a substantial advancement in federated learning deployments in edge computing ecosystems:

• Resource Efficiency: It ensures more efficient utilization of constrained resources like bandwidth and computational power at edge nodes, which is critical for scalability in IoT applications.
• Real-time Adaptation: The algorithm's ability to adapt to real-time system dynamics and data distributions enhances its robustness in unpredictable environments.

Future Directions

Possible future research avenues include:

• Extending the convergence analysis to cover broader non-convex models, such as deeper neural networks.
• Investigating asynchronous variants of the proposed adaptive mechanism to further capitalize on heterogeneous resource capabilities.
• Incorporating advanced compression/quantization techniques during parameter exchanges to further reduce communication overhead.

In summary, this work significantly contributes to the field of federated learning by providing a theoretically grounded, adaptive approach to optimizing resource usage in decentralized, resource-constrained environments. The insights and methodologies introduced have tangible implications for the deployment and scalability of federated learning systems in real-world edge computing scenarios.