DICE: Data Influence Cascade in Decentralized Learning

Tongtian Zhu, Wenhao Li, Can Wang, Fengxiang He

Keywords: Decentralized Learning, Data Influence, Data Valuation, Contribution Attribution, Incentive Mechanism

TL;DR: We introduce DICE, the first framework for quantifying data influence in fully decentralized learning. Our analysis shows that data influence cascades through the communication network like ripples, shaped by data, communication, and loss curvature.

Abstract:

Decentralized learning offers a promising approach to crowdsource data consumptions and computational workloads across geographically distributed compute interconnected through peer-to-peer networks, accommodating the exponentially increasing demands. However, proper incentives are still in absence, considerably discouraging participation. Our vision is that a fair incentive mechanism relies on fair attribution of contributions to participating nodes, which faces non-trivial challenges arising from the localized connections making influence ``cascade'' in a decentralized network. To overcome this, we design the first method to estimate Data Influence CascadE (DICE) in a decentralized environment. Theoretically, the framework derives tractable approximations of influence cascade over arbitrary neighbor hops, suggesting the influence cascade is determined by an interplay of data, communication topology, and the curvature of loss landscape.DICE also lays the foundations for applications including selecting suitable collaborators and identifying malicious behaviors. Project page is available at https://raiden-zhu.github.io/blog/2025/DICE.

Primary Area: interpretability and explainable AI

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Submission Number: 7615