Harnessing Federated Generative Learning for Green and Sustainable Internet of Things

• URL: http://arxiv.org/abs/2407.05915v1
• Date: Tue, 30 Apr 2024 17:15:26 GMT
• Title: Harnessing Federated Generative Learning for Green and Sustainable Internet of Things
• Authors: Yuanhang Qi, M. Shamim Hossain,
• Abstract summary: One-shot Federated Learning (OSFL) is 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. 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.
• Score: 9.699977999019977
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

Related papers

• Green Federated Learning: A new era of Green Aware AI [11.536626349203361]
  Federated Learning (FL) presents new opportunities to address this need. It's crucial to furnish researchers, stakeholders, and interested parties with a roadmap to navigate and understand existing efforts and gaps in green-aware AI algorithms. This survey primarily aims to achieve this objective by identifying and analyzing over a hundred FL works.
  arXiv  Detail & Related papers  (2024-09-19T09:54:18Z)
• Towards Sustainable IoT: Challenges, Solutions, and Future Directions for Device Longevity [0.0]
  This study explores the various complex difficulties which contributed to the early decommissioning of IoT devices. By examining factors such as security vulnerabilities, user awareness gaps, and the influence of fashion-driven technology trends, the paper underscores the need for legislative interventions.
  arXiv  Detail & Related papers  (2024-05-26T04:05:01Z)
• Powering the Future of IoT: Federated Learning for Optimized Power Consumption and Enhanced Privacy [0.0]
  Federated Learning emerges as a promising paradigm to address the inherent challenges of power consumption and data privacy in IoT environments. This paper explores the transformative potential of FL in enhancing the longevity of IoT devices by mitigating power consumption and enhancing privacy and security measures.
  arXiv  Detail & Related papers  (2024-05-05T22:18:22Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges [55.82853124625841]
  Artificial General Intelligence (AGI) possesses the capacity to comprehend, learn, and execute tasks with human cognitive abilities. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the Internet of Things. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education.
  arXiv  Detail & Related papers  (2023-09-14T05:43:36Z)
• Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions [78.5371215066019]
  Over-the-air federated learning (OTA-FL) enables users at the network edge to share spectrum resources and achieves efficient and low-latency global model aggregation. This paper provides a holistic review of progress in OTA-FL and points to potential future research directions.
  arXiv  Detail & Related papers  (2023-07-03T12:44:52Z)
• Multi-Tier Hierarchical Federated Learning-assisted NTN for Intelligent IoT Services [24.10349383347469]
  This study explores MT-HFL's role in fostering a decentralized, collaborative learning environment. It enables IoT devices to not only contribute but also make informed decisions in network management. This setup ensures efficient data handling, advanced privacy and security measures, and responsive to fluctuating network conditions.
  arXiv  Detail & Related papers  (2023-05-09T14:03:22Z)
• Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs [62.91362897985057]
  An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semi-autonomous IoT applications. This paper provides a state-of-the-art overview of these technologies and illustrates their functionality and performance, with special attention to the tradeoff among resources, latency, privacy and energy consumption.
  arXiv  Detail & Related papers  (2021-10-04T19:41:42Z)
• To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices [78.38046945665538]
  federated learning (FL) over massive mobile edge devices opens new horizons for numerous intelligent mobile applications. FL imposes huge communication and computation burdens on participating devices due to periodical global synchronization and continuous local training. We develop a convergence-guaranteed FL algorithm enabling flexible communication compression.
  arXiv  Detail & Related papers  (2020-12-22T02:54:18Z)
• Green Internet of Things: The Next Generation Energy Efficient Internet of Things [0.0]
  The Internet of Things (IoT) is a novel technical paradigm aimed at enabling connectivity between billions of interconnected devices all around the world. This IoT is being served in various domains, such as smart healthcare, traffic surveillance, smart homes, smart cities, and various industries. The Green IoT envisages reducing the energy consumption of IoT devices and keeping the environment safe and clean.
  arXiv  Detail & Related papers  (2020-12-02T16:52:18Z)
• Wireless Communications for Collaborative Federated Learning [160.82696473996566]
  Internet of Things (IoT) devices may not be able to transmit their collected data to a central controller for training machine learning models. Google's seminal FL algorithm requires all devices to be directly connected with a central controller. This paper introduces a novel FL framework, called collaborative FL (CFL), which enables edge devices to implement FL with less reliance on a central controller.
  arXiv  Detail & Related papers  (2020-06-03T20:00:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.