Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices

Zhao, Yang; Zhao, Jun; Jiang, Linshan; Tan, Rui; Niyato, Dusit; Li, Zengxiang; Lyu, Lingjuan; Liu, Yingbo

doi:10.48550/arxiv.1906.10893

Cited by 8 publications

(1 citation statement)

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“…However, due to stastical heterogeneity and high communication costs, P2P verification may not always be suitable. In [19] and [20], the authors presented a blockchain-based reputation system for FL, where a UE's reputation is traceable. In [20], the authors presented a reputation-based UE selection using subjective logic.…”

Section: Related Workmentioning

confidence: 99%

Data-Quality Based Scheduling for Federated Edge Learning

Taik,

Moudoud,

Cherkaoui

2022

Preprint

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FEderated Edge Learning (FEEL) has emerged as a leading technique for privacy-preserving distributed training in wireless edge networks, where edge devices collaboratively train machine learning (ML) models with the orchestration of a server. However, due to frequent communication, FEEL needs to be adapted to the limited communication bandwidth. Furthermore, the statistical heterogeneity of local datasets' distributions, and the uncertainty about the data quality pose important challenges to the training's convergence. Therefore, a meticulous selection of the participating devices and an analogous bandwidth allocation are necessary. In this paper, we propose a data-quality based scheduling (DQS) algorithm for FEEL. DQS prioritizes reliable devices with rich and diverse datasets. In this paper, we define the different components of the learning algorithm and the data-quality evaluation. Then, we formulate the device selection and the bandwidth allocation problem. Finally, we present our DQS algorithm for FEEL, and we evaluate it in different data poisoning scenarios.

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Section: Related Workmentioning

confidence: 99%