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.

翻译：随着去中心化与机会主义机器学习方法的兴起，终端设备越来越多地被要求利用自身收集的众包数据，在设备本地训练深度学习模型。这类方法在资源消耗与隐私保护两方面均具有优势。当设备能直接从训练后的模型中获益时，激励是隐性的——协作带来的高精度模型可用性，本身便激励设备贡献资源。然而，当终端用户设备被要求贡献其计算、通信与数据等资源，以执行主要为他人（例如为邻居设备所需的某一任务训练模型，而设备所有者对该任务缺乏兴趣）谋利的任务时，必须提供显式激励机制。在本项目中，我们提出了一种基于区块链的新型激励机制，适用于完全去中心化与机会主义的学习架构。我们利用智能合约，不仅为参与去中心化学习的终端设备提供显式激励，还构建了一个完全去中心化的机制，用于检查并反思学习架构的行为。