Federated Learning for Internet of Things: A Comprehensive Survey

@article{Nguyen2021FederatedLF,
  title={Federated Learning for Internet of Things: A Comprehensive Survey},
  author={Dinh C. Nguyen and Ming Ding and Pubudu N. Pathirana and Aruna Prasad Seneviratne and Jun Li and H. Vincent Poor},
  journal={IEEE Communications Surveys \& Tutorials},
  year={2021},
  volume={23},
  pages={1622-1658}
}
The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may not be feasible in realistic application scenarios due to the high scalability of modern IoT networks and growing data privacy concerns. Federated Learning (FL) has emerged as a distributed collaborative AI approach that can… 

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References

SHOWING 1-10 OF 205 REFERENCES

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey

An extensive survey on the use of blockchain and AI for combating COVID-19 epidemics and points out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

Exploiting Unlabeled Data in Smart Cities using Federated Edge Learning

This paper proposes a semi-supervised federated edge learning method called FedSem that exploits unlabeled data in real-time that can achieve accuracy by up to 8% by utilizing the unlabeling data in the learning process.

Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework With UAV Swarms

A new federated learning (FL)-based aerial-ground air quality sensing framework for fine-grained 3-D air quality monitoring and forecasting that leverages a lightweight Dense-MobileNet model to achieve energy-efficient end-to-end learning from haze features of haze images taken by unmanned aerial vehicles (UAVs) for predicting AQI scale distribution.

Task Allocation for Mobile Federated and Offloaded Learning with Energy and Delay Constraints

The aim is to maximize learning accuracy while guaranteeing that the total time taken and energy consumed by each learner in the system are bounded by a preset duration and maximum energy, respectively, while taking into account heterogeneous communication and communication capabilities of the channels and nodes.

FDA$^3$: Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications

Inspired by federated learning, the proposed cloud-based architecture enables the sharing of defense capabilities against different attacks among IIoT devices, and shows that the generated DNNs by the approach can not only resist more malicious attacks than existing attack-specific adversarial training methods, but also prevent IIaT applications from new attacks.

Learning Cooperation Schemes for Mobile Edge Computing Empowered Internet of Vehicles

Intelligent Transportation System has emerged as a promising paradigm providing efficient traffic management while enabling innovative transport services. The implementation of ITS always demands

Pseudo Label-Driven Federated Learning-Based Decentralized Indoor Localization via Mobile Crowdsourcing

The experiment results demonstrate that the proposed CRNP enables to improve the indoor localization accuracy by using unlabeled crowdsourced data and the designed decentralized scheme is robust to different data distribution and is capable to reduce the network cost and prevent users’ privacy leakage.

Semisupervised Distributed Learning With Non-IID Data for AIoT Service Platform

An edge learning system based on semisupervised learning and federated learning technologies that can have up to 5.9% higher accuracy of object detection for the video analysis applications by fully utilizing unlabeled data, compared with the situation that only labeled data are used.

Federated Learning With Blockchain for Autonomous Vehicles: Analysis and Design Challenges

This work demonstrates that the proposed idea of tuning the block arrival rate is provably online and capable of driving the system dynamics to the desired operating point and identifies the improved dependency on other blockchain parameters for a given set of channel conditions, retransmission limits, and frame sizes.

Distributed Sensing Using Smart End-User Devices: Pathway to Federated Learning for Autonomous IoT

  • Ahmed ImteajM. Amini
  • Computer Science
    2019 International Conference on Computational Science and Computational Intelligence (CSCI)
  • 2019
This study proposes a distributed sensing approach that is capable to identify a device using token, can activate distributed end-user devices to send data to the cloud whenever it requires and store data in the cloud server maintaining proper format.
...