  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.
