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ASA: Adaptive Smart Agent Federated Learning via Device-Aware Clustering for Heterogeneous IoT

Ali Salimi, Saadat Izadi, Mahmood Ahmadi, Hadi Tabatabaee Malazi

TL;DR

This work introduces ASA (Adaptive Smart Agent), a new framework that clusters devices adaptively based on real-time resource profiles and adapts customized models suited to every cluster's capability, rendering it a suitable answer for real-world IoT apps.

Abstract

Federated learning (FL) has become a promising answer to facilitating privacy-preserving collaborative learning in distributed IoT devices. However, device heterogeneity is a key challenge because IoT networks include devices with very different computational powers, memory availability, and network environments. To this end, we introduce ASA (Adaptive Smart Agent). This new framework clusters devices adaptively based on real-time resource profiles and adapts customized models suited to every cluster's capability. ASA capitalizes on an intelligent agent layer that examines CPU power, available memory, and network environment to categorize devices into three levels: high-performance, mid-tier, and low-capability. Each level is provided with a model tuned to its computational power to ensure inclusive engagement across the network. Experimental evaluation on two benchmark datasets, MNIST and CIFAR-10, proves that ASA decreases communication burden by 43% to 50%, improves resource utilization by 43%, and achieves final model accuracies of 98.89% on MNIST and 85.30% on CIFAR-10. These results highlight ASA's efficacy in enhancing efficiency, scalability, and fairness in heterogeneous FL environments, rendering it a suitable answer for real-world IoT apps.

ASA: Adaptive Smart Agent Federated Learning via Device-Aware Clustering for Heterogeneous IoT

TL;DR

This work introduces ASA (Adaptive Smart Agent), a new framework that clusters devices adaptively based on real-time resource profiles and adapts customized models suited to every cluster's capability, rendering it a suitable answer for real-world IoT apps.

Abstract

Federated learning (FL) has become a promising answer to facilitating privacy-preserving collaborative learning in distributed IoT devices. However, device heterogeneity is a key challenge because IoT networks include devices with very different computational powers, memory availability, and network environments. To this end, we introduce ASA (Adaptive Smart Agent). This new framework clusters devices adaptively based on real-time resource profiles and adapts customized models suited to every cluster's capability. ASA capitalizes on an intelligent agent layer that examines CPU power, available memory, and network environment to categorize devices into three levels: high-performance, mid-tier, and low-capability. Each level is provided with a model tuned to its computational power to ensure inclusive engagement across the network. Experimental evaluation on two benchmark datasets, MNIST and CIFAR-10, proves that ASA decreases communication burden by 43% to 50%, improves resource utilization by 43%, and achieves final model accuracies of 98.89% on MNIST and 85.30% on CIFAR-10. These results highlight ASA's efficacy in enhancing efficiency, scalability, and fairness in heterogeneous FL environments, rendering it a suitable answer for real-world IoT apps.
Paper Structure (28 sections, 14 equations, 11 figures, 2 tables)

This paper contains 28 sections, 14 equations, 11 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Proposed ASA Framework
  4. An Overview and Architecture of the ASA Framework
  5. Theoretical foundations and analysis
  6. System Model and Notations
  7. Problem formulation
  8. Computational Complexity
  9. Design and Implementation Details
  10. System Design and Components
  11. Resource Management and Optimization
  12. Adaptive Model Distribution
  13. Training Coordination and Synchronization
  14. Evaluation and Discussion
  15. Configurations and parameters
  16. ...and 13 more sections

Figures (11)

  • Figure 1: Cloud-to-edge federated model training and deployment.
  • Figure 2: Internal architecture of the Adaptive Smart Agent (ASA) framework.
  • Figure 3: (a) Cumulative and (b) average communication costs, showing ASA's superior efficiency compared to FedAvg, HierFL, and FedProx on the CIFAR-10 dataset.
  • Figure 4: Comparative analysis of ASA and baseline methods on CIFAR-10: (a) Convergence accuracy, (b) Communication cost, (c) Convergence speed.
  • Figure 5: Scalability analysis of ASA across different metrics ((a) accuracy, (b) communication cost, (c) training time, and (d) memory usage) with varying numbers of devices on the CIFAR-10 dataset.
  • ...and 6 more figures