Harnessing Federated Generative Learning for Green and Sustainable Internet of Things
Yuanhang Qi, M. Shamim Hossain

TL;DR
This paper presents One-shot Federated Learning (OSFL), a novel approach that reduces energy consumption and enhances privacy in IoT by condensing multiple communication rounds into a single operation, promoting sustainability.
Contribution
Introduction of OSFL, a new federated learning paradigm that minimizes communication rounds and resource use, integrating generative learning for privacy and sustainability in IoT.
Findings
OSFL significantly reduces energy consumption and communication overhead.
OSFL maintains robust data privacy through generative learning techniques.
The approach extends device lifespan and lowers environmental impact.
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…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Error Correcting Code Techniques · Wireless Communication Security Techniques
