Exploring Deep-Reinforcement-Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Preserving EdgeIoT

@article{Zheng2022ExploringDF,
  title={Exploring Deep-Reinforcement-Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Preserving EdgeIoT},
  author={Jingjing Zheng and Kai Li and Naram Mhaisen and Wei Ni and Eduardo Tovar and Mohsen Guizani},
  journal={IEEE Internet of Things Journal},
  year={2022},
  volume={9},
  pages={21099-21110}
}
Federated learning (FL) has been increasingly considered to preserve data training privacy from eavesdropping attacks in mobile-edge computing-based Internet of Things (EdgeIoT). On the one hand, the learning accuracy of FL can be improved by selecting the IoT devices with large data sets for training, which gives rise to a higher energy consumption. On the other hand, the energy consumption can be reduced by selecting the IoT devices with small data sets for FL, resulting in a falling learning… 

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References

SHOWING 1-10 OF 48 REFERENCES

Experience-Driven Computational Resource Allocation of Federated Learning by Deep Reinforcement Learning

This paper designs an experience-driven algorithm based on the Deep Reinforcement Learning (DRL), which can converge to the near-optimal solution without knowledge of network quality and outperforms the start-of-the-art by 40% at most.

A Learning-Based Incentive Mechanism for Federated Learning

The incentive mechanism for federated learning to motivate edge nodes to contribute model training is studied and a deep reinforcement learning-based (DRL) incentive mechanism has been designed to determine the optimal pricing strategy for the parameter server and the optimal training strategies for edge nodes.

Multiagent DDPG-Based Deep Learning for Smart Ocean Federated Learning IoT Networks

A novel multiagent deep reinforcement learning-based algorithm which can realize federated learning (FL) computation with Internet-of-Underwater-Things (IoUT) devices in the ocean environment and achieves 80% and 41% performance improvements than the standard actor–critic and DDPG, respectively, in terms of the downlink throughput.

Online Client Scheduling for Fast Federated Learning

This letter reformulates the client scheduling problem as a multi-armed bandit program and proposes an online scheduling scheme based on $\epsilon $ -greedy algorithm to achieve a tradeoff between exploration and exploitation.

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

A convergence rate characterizing the trade-off between local computation rounds of each UE to update its local model and global communication rounds to update the FL global model is provided, and experimental results show that FEDL outperforms the vanilla FedAvg algorithm in terms of convergence rate and test accuracy in various settings.

FedMCCS: Multicriteria Client Selection Model for Optimal IoT Federated Learning

FedMCCS is proposed, a multicriteria-based approach for client selection in federated learning that outperforms the other approaches by reducing the number of communication rounds to reach the intended accuracy and handling the least number of discarded rounds.

Adaptive Federated Learning in Resource Constrained Edge Computing Systems

This paper analyzes the convergence bound of distributed gradient descent from a theoretical point of view, and proposes a control algorithm that determines the best tradeoff between local update and global parameter aggregation to minimize the loss function under a given resource budget.

Federated Learning for Energy-balanced Client Selection in Mobile Edge Computing

A new FL-based client selection optimization is proposed to balance the trade-off between energy consumption of the edge clients and the learning accuracy of FL, and it is shown that this optimization problem is NP-complete.

A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond

A new classification of FL topics and research fields is provided based on thorough analysis of the main technical challenges and current related work, and comprehensive taxonomies covering various challenging aspects, contributions, and trends in the literature are elaborate.

Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach

This work proposes the adoption of a Federated Learning (FL) based approach to enable privacy-preserving collaborative Machine Learning across a federation of independent DaaS providers for the development of IoV applications, e.g., for traffic prediction and car park occupancy management.