iDML: Incentivized Decentralized Machine Learning
This addresses the challenge of resource contribution in decentralized ML for scenarios where devices lack implicit incentives, offering a solution for collaborative but non-beneficial tasks.
The paper tackles the problem of incentivizing end-user devices to contribute resources for decentralized machine learning tasks that primarily benefit others, by proposing a novel blockchain-based incentive mechanism that also enables decentralized inspection of the learning architecture.
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.