Session cloning protocol between smart devices for connected and un-connected states in B4G and 5G

Vrind, Tushar; Sharma, Diwakar; Pathak, Lalit; Das, Debabrata

doi:10.1109/ccnc.2018.8319191

Cited by 1 publication

(1 citation statement)

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“…To address these issues, numerous approaches have been proposed. In [13],the authors proposed a novel Federated Learning by Aerial-Assisted Protocol (FLAP) that enables a higher accuracy for an image classification model .In [14], the authors design a federated learning model to coordinate x-applications to improve learning efficiency. In [15],the authors present a federated learning security and privacy model enabled by blockchain technology to ensure that it can be used normally for the protection of user data.…”

Section: Related Workmentioning

confidence: 99%

A DQN-based Multi-Objective Participant Selection for Efficient Federated Learning

Xu¹,

Liu²,

Ma³

et al. 2023

Preprint

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As a new distributed machine learning (ML) approach, federated learning (FL) shows the great potential to preserve data privacy by enabling distributed data owners to collaboratively build a global model without sharing their raw data. However, the heterogeneity in terms of data distribution and hardware configurations make it hard to select participants from the thousands of nodes. In this paper, we propose a multi-objective node selection approach to improve time-to-accuracy performance while resisting malicious nodes. We firstly design a deep reinforcement learning assisted FL framework. Then the problem of multi-objective node selection under this framework is formulated as a Markov decision process (MDP), which aims to reduce the training time and improve model accuracy simultaneously. Finally, a deep Q-netwok (DQN) based algorithm is proposed to efficiently solve the optimal set of participants for each iteration. Simulation results show that the proposed method not only significantly improves the accuracy and training speed of FL, but has stronger robustness to resist malicious nodes.

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Section: Related Workmentioning

confidence: 99%