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REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments

Humaid Ahmed Desai, Amr Hilal, Hoda Eldardiry

TL;DR

A novel approach specifically devised to address challenges in resource-limited devices, which preserves data privacy and performance standards but also accommodates heterogeneous model architectures, facilitating the participation of a broader array of diverse client devices in the training process, all while consuming minimal bandwidth.

Abstract

Federated Learning (FL) plays a critical role in distributed systems. In these systems, data privacy and confidentiality hold paramount importance, particularly within edge-based data processing systems such as IoT devices deployed in smart homes. FL emerges as a privacy-enforcing sub-domain of machine learning that enables model training on client devices, eliminating the necessity to share private data with a central server. While existing research has predominantly addressed challenges pertaining to data heterogeneity, there remains a current gap in addressing issues such as varying device capabilities and efficient communication. These unaddressed issues raise a number of implications in resource-constrained environments. In particular, the practical implementation of FL-based IoT or edge systems is extremely inefficient. In this paper, we propose "Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments (REFT)," a novel approach specifically devised to address these challenges in resource-limited devices. Our proposed method uses Variable Pruning to optimize resource utilization by adapting pruning strategies to the computational capabilities of each client. Furthermore, our proposed REFT technique employs knowledge distillation to minimize the need for continuous bidirectional client-server communication. This achieves a significant reduction in communication bandwidth, thereby enhancing the overall resource efficiency. We conduct experiments for an image classification task, and the results demonstrate the effectiveness of our approach in resource-limited settings. Our technique not only preserves data privacy and performance standards but also accommodates heterogeneous model architectures, facilitating the participation of a broader array of diverse client devices in the training process, all while consuming minimal bandwidth.

REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments

TL;DR

A novel approach specifically devised to address challenges in resource-limited devices, which preserves data privacy and performance standards but also accommodates heterogeneous model architectures, facilitating the participation of a broader array of diverse client devices in the training process, all while consuming minimal bandwidth.

Abstract

Federated Learning (FL) plays a critical role in distributed systems. In these systems, data privacy and confidentiality hold paramount importance, particularly within edge-based data processing systems such as IoT devices deployed in smart homes. FL emerges as a privacy-enforcing sub-domain of machine learning that enables model training on client devices, eliminating the necessity to share private data with a central server. While existing research has predominantly addressed challenges pertaining to data heterogeneity, there remains a current gap in addressing issues such as varying device capabilities and efficient communication. These unaddressed issues raise a number of implications in resource-constrained environments. In particular, the practical implementation of FL-based IoT or edge systems is extremely inefficient. In this paper, we propose "Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments (REFT)," a novel approach specifically devised to address these challenges in resource-limited devices. Our proposed method uses Variable Pruning to optimize resource utilization by adapting pruning strategies to the computational capabilities of each client. Furthermore, our proposed REFT technique employs knowledge distillation to minimize the need for continuous bidirectional client-server communication. This achieves a significant reduction in communication bandwidth, thereby enhancing the overall resource efficiency. We conduct experiments for an image classification task, and the results demonstrate the effectiveness of our approach in resource-limited settings. Our technique not only preserves data privacy and performance standards but also accommodates heterogeneous model architectures, facilitating the participation of a broader array of diverse client devices in the training process, all while consuming minimal bandwidth.
Paper Structure (20 sections, 6 equations, 6 figures, 1 table, 1 algorithm)

This paper contains 20 sections, 6 equations, 6 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Efficient Federated Learning
  4. Model Compression
  5. Knowledge Distillation
  6. Preliminaries
  7. Proposed Framework: REFT
  8. Variable Pruning
  9. Knowledge Distillation
  10. Variable Pruning vs. Static Pruning for Resource Utilization
  11. Experiments
  12. Datasets and models
  13. Baselines
  14. Metrics
  15. Implementation Details
  16. ...and 5 more sections

Figures (6)

  • Figure 1: Overview of REFT
  • Figure 2: Comparing client accuracies: static pruning (FL-PQSU and PruneFL) vs. REFT on the VGG-16 model.
  • Figure 3: Effect of pruning on GPU utilization and training time of VGG-16.
  • Figure 4: Effect of pruning on test accuracy of VGG-16.
  • Figure 5: Reduction in model size and FLOPs of VGG-16 by REFT.
  • ...and 1 more figures