Federated learning meets blockchain at 6G edge: a drone-assisted networking for disaster response

@article{Pokhrel2020FederatedLM,
  title={Federated learning meets blockchain at 6G edge: a drone-assisted networking for disaster response},
  author={Shiva Raj Pokhrel},
  journal={Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond},
  year={2020}
}
  • Shiva Raj Pokhrel
  • Published 25 September 2020
  • Computer Science
  • Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond
We consider a new blockchain empowered federated learning approach which uses wireless mobile miners at drones in the future sixth generation (6G) networks for a disaster response system. Our focus is on the blockchain latency, and energy consumption in the proposed architecture of the network of drones. Maintaining low delay in wireless communication between the drones is required to minimize blockchain forking events while performing blockchain operations. Therefore, we quantify the… 

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References

SHOWING 1-10 OF 20 REFERENCES

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.

Performance Analysis of Blockchain Systems With Wireless Mobile Miners

Simulation results verify the analytical derivations and show that using a larger number of MMs can reduce the energy consumption by up to 95% compared to a blockchain system with a single MM.

WITHDRAWN: Towards efficient and reliable federated learning using Blockchain for autonomous vehicles

  • Shiva Raj Pokhrel
  • Computer Science
  • 2020

When Mobile Blockchain Meets Edge Computing

A novel concept of edge computing for mobile blockchain and an economic approach for edge computing resource management are introduced and a prototype of mobile edge computing enabled blockchain systems are presented with experimental results to justify the proposed concept.

Bitcoin-NG: A Scalable Blockchain Protocol

This paper implements Bitcoin-NG, a new blockchain protocol designed to scale, which is Byzantine fault tolerant, is robust to extreme churn, and shares the same trust model obviating qualitative changes to the ecosystem.

A decentralized solution for IoT data trusted exchange based-on blockchain

This paper proposes a decentralized solution based on the blockchain for IoT data trusted exchange and realizes a prototype by using Ethereum blockchain and smart contracts and presents its auditable, transparent, decentralized features visually.

Federated Learning for Ultra-Reliable Low-Latency V2V Communications

It is shown that FL enables the proposed distributed method to estimate the tail distribution of queues with an accuracy that is very close to a centralized solution with up to 79% reductions in the amount of data that need to be exchanged.

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

In a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved, this raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale.

Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges

An accessible introduction to the general idea of federated learning is provided, several possible applications in 5G networks are discussed, and key technical challenges and open problems for future research on Federated learning in the context of wireless communications are described.

Effects of Heterogeneous Mobility on D2D- and Drone-Assisted Mission-Critical MTC in 5G

It is established that the availability of alternative connectivity options, such as D2D links and drone-assisted access, helps meet the requirements of mcMTC applications in a wide range of scenarios, including industrial automation, vehicular connectivity, and urban communications.