mlsanityData to 7 Oct 2026

A Framework for Incentivized Collaborative Learning

arXiv 2305.17052 ↗First posted 26 May 2023cs.LG cs.AI cs.CYcited by 2

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Abstract (arXiv)

Collaborations among various entities, such as companies, research labs, AI agents, and edge devices, have become increasingly crucial for achieving machine learning tasks that cannot be accomplished by a single entity alone. This is likely due to factors such as security constraints, privacy concerns, and limitations in computation resources. As a result, collaborative learning (CL) research has been gaining momentum. However, a significant challenge in practical applications of CL is how to effectively incentivize multiple entities to collaborate before any collaboration occurs. In this study, we propose ICL, a general framework for incentivized collaborative learning, and provide insights into the critical issue of when and why incentives can improve collaboration performance. Furthermore, we show the broad applicability of ICL to specific cases in federated learning, assisted learning, and multi-armed bandit with both theory and experimental results.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 41 of its 79 references that are in the map. First posted on arXiv 26 May 2023.

Builds on

41 of 79 references.

References · 41

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29 more
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Cited by year

Citing papers per year: 2023: 1; 2024: 0; 2025: 1.

2 papers.

  1. 2025DICE: Data Influence Cascade in Decentralized Learningcited by 2
  2. 2023Incentivized Collaboration in Active Learningcited by 0