Report
Harnessing Federated Generative Learning for Green and Sustainable Internet of Things
| العنوان: | Harnessing Federated Generative Learning for Green and Sustainable Internet of Things |
|---|---|
| المؤلفون: | Qi, Yuanhang, Hossain, M. Shamim |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Networking and Internet Architecture, Computer Science - Artificial Intelligence |
| الوصف: | 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. Comment: This paper is a correction of the published version, in which we corrected the grammatical errors between contexts and highlighted the relationship with "Federated generative learning with foundation models" |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.05915 |
| رقم الانضمام: | edsarx.2407.05915 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |