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arXiv · 2304.05354

iDML: Incentivized Decentralized Machine Learning

Haoxiang Yu·Hsiao-Yuan Chen·Sangsu Lee·Sriram Vishwanath·Xi Zheng·Christine Julien
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Abstract

With the rising emergence of decentralized and opportunistic approaches to machine learning, end devices are increasingly tasked with training deep learning models on-devices using crowd-sourced data that they collect themselves. These approaches are desirable from a resource consumption perspective and also from a privacy preservation perspective. When the devices benefit directly from the trained models, the incentives are implicit - contributing devices' resources are incentivized by the availability of the higher-accuracy model that results from collaboration. However, explicit incentive mechanisms must be provided when end-user devices are asked to contribute their resources (e.g., computation, communication, and data) to a task performed primarily for the benefit of others, e.g., training a model for a task that a neighbor device needs but the device owner is uninterested in. In this project, we propose a novel blockchain-based incentive mechanism for completely decentralized and opportunistic learning architectures. We leverage a smart contract not only for providing explicit incentives to end devices to participate in decentralized learning but also to create a fully decentralized mechanism to inspect and reflect on the behavior of the learning architecture.

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Haoxiang Yu, Hsiao-Yuan Chen, Sangsu Lee, Sriram Vishwanath, Xi Zheng, Christine Julien. 2023-04-10. iDML: Incentivized Decentralized Machine Learning. https://arxiv.org/abs/2304.05354

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