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Harnessing Federated Generative Learning for Green and Sustainable Internet of Things

Yuanhang Qi, M. Shamim Hossain

TL;DR

This work tackles the environmental and privacy challenges of widespread IoT deployment by introducing One-shot Federated Learning ($OSFL$) and Federated Generative Learning ($FGL$). OSFL drastically reduces communication rounds to a single exchange, while FGL uses server-side generative models and client prompts to synthesize surrogate data for global training, preserving data privacy. The authors propose two local prompt strategies, demonstrate a complete one-shot FL pipeline augmented by diffusion-based data synthesis, and validate on Fashion-MNIST, CIFAR-10, and CIFAR-100, showing competitive accuracy with substantially reduced communication and energy costs. The approach offers a practical path to green IoT, enabling scalable, privacy-preserving learning in resource-constrained environments with broad applicability to smart cities, healthcare, and industrial automation.

Abstract

The rapid proliferation of devices in the Internet of Things (IoT) has ushered in a transformative era of data-driven connectivity across various domains. However, this exponential growth has raised pressing concerns about environmental sustainability and data privacy. In response to these challenges, this paper introduces One-shot Federated Learning (OSFL), an innovative paradigm that harmonizes sustainability and machine learning within IoT ecosystems. OSFL revolutionizes the traditional Federated Learning (FL) workflow by condensing multiple iterative communication rounds into a single operation, thus significantly reducing energy consumption, communication overhead, and latency. This breakthrough is coupled with the strategic integration of generative learning techniques, ensuring robust data privacy while promoting efficient knowledge sharing among IoT devices. By curtailing resource utilization, OSFL aligns seamlessly with the vision of green and sustainable IoT, effectively extending device lifespans and mitigating their environmental footprint. Our research underscores the transformative potential of OSFL, poised to reshape the landscape of IoT applications across domains such as energy-efficient smart cities and groundbreaking healthcare solutions. This contribution marks a pivotal step towards a more responsible, sustainable, and technologically advanced future.

Harnessing Federated Generative Learning for Green and Sustainable Internet of Things

TL;DR

This work tackles the environmental and privacy challenges of widespread IoT deployment by introducing One-shot Federated Learning () and Federated Generative Learning (). OSFL drastically reduces communication rounds to a single exchange, while FGL uses server-side generative models and client prompts to synthesize surrogate data for global training, preserving data privacy. The authors propose two local prompt strategies, demonstrate a complete one-shot FL pipeline augmented by diffusion-based data synthesis, and validate on Fashion-MNIST, CIFAR-10, and CIFAR-100, showing competitive accuracy with substantially reduced communication and energy costs. The approach offers a practical path to green IoT, enabling scalable, privacy-preserving learning in resource-constrained environments with broad applicability to smart cities, healthcare, and industrial automation.

Abstract

The rapid proliferation of devices in the Internet of Things (IoT) has ushered in a transformative era of data-driven connectivity across various domains. However, this exponential growth has raised pressing concerns about environmental sustainability and data privacy. In response to these challenges, this paper introduces One-shot Federated Learning (OSFL), an innovative paradigm that harmonizes sustainability and machine learning within IoT ecosystems. OSFL revolutionizes the traditional Federated Learning (FL) workflow by condensing multiple iterative communication rounds into a single operation, thus significantly reducing energy consumption, communication overhead, and latency. This breakthrough is coupled with the strategic integration of generative learning techniques, ensuring robust data privacy while promoting efficient knowledge sharing among IoT devices. By curtailing resource utilization, OSFL aligns seamlessly with the vision of green and sustainable IoT, effectively extending device lifespans and mitigating their environmental footprint. Our research underscores the transformative potential of OSFL, poised to reshape the landscape of IoT applications across domains such as energy-efficient smart cities and groundbreaking healthcare solutions. This contribution marks a pivotal step towards a more responsible, sustainable, and technologically advanced future.
Paper Structure (20 sections, 7 equations, 7 figures, 5 tables, 2 algorithms)

This paper contains 20 sections, 7 equations, 7 figures, 5 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Federated Learning in IoT
  4. Sustainability in IoT
  5. One-shot Learning in FL
  6. Generative Learning in FL
  7. Preliminaries
  8. Federated Learning
  9. Text-to-Image Generative Models
  10. Methodology
  11. Overview of Federated Generative Learning Framework
  12. Local Prompt Generation
  13. Training Data Synthesis
  14. One-shot Learning
  15. Complexity Analysis
  16. ...and 5 more sections

Figures (7)

  • Figure 1: An overview of the One-shot FGL system.
  • Figure 2: Local prompt generation strategies which are proposed by Zhang et al zhang2023federated.
  • Figure 3: Communication efficiency evaluation on IID setting.
  • Figure 4: Communication efficiency evaluation on non-IID setting.
  • Figure 5: Running time reults on IID setting.
  • ...and 2 more figures