Incentivizing Truthfulness and Collaborative Fairness in Bayesian Learning

• URL: http://arxiv.org/abs/2605.11889v1
• Date: Tue, 12 May 2026 10:02:23 GMT
• Title: Incentivizing Truthfulness and Collaborative Fairness in Bayesian Learning
• Abstract summary: Collaborative machine learning involves training high-quality models using datasets from a number of sources.<n>To incentivize sources to share data, existing data valuation methods fairly reward each source based on its data submitted as is.<n>This paper presents the first mechanism that ensures (F) collaborative fairness and incentivizes (T) truthfulness at equilibrium for Bayesian models.
• Score: 68.76184067877499
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Collaborative machine learning involves training high-quality models using datasets from a number of sources. To incentivize sources to share data, existing data valuation methods fairly reward each source based on its data submitted as is. However, as these methods do not verify nor incentivize data truthfulness, the sources can manipulate their data (e.g., by submitting duplicated or noisy data) to artificially increase their valuations and rewards or prevent others from benefiting. This paper presents the first mechanism that provably ensures (F) collaborative fairness and incentivizes (T) truthfulness at equilibrium for Bayesian models. Our mechanism combines semivalues (e.g., Shapley value), which ensure fairness, and a truthful data valuation function (DVF) based on a validation set that is unknown to the sources. As semivalues are influenced by others' data, we introduce an additional condition to prove that a source can maximize its expected data values in coalitions and semivalues by submitting a dataset that captures its true knowledge. Additionally, we discuss the implications and suitable relaxations of (F) and (T) when the mediator has a limited budget for rewards or lacks a validation set. Our theoretical findings are validated on synthetic and real-world datasets.

Related papers

• A Cramér-von Mises Approach to Incentivizing Truthful Data Sharing [10.731682970668142]
  We develop reward mechanisms based on a novel, two-sample test inspired by the Cram'er-von Mises statistic.<n>Our methods strictly incentivize agents to submit more genuine data, while disincentivizing data fabrication and other types of untruthful reporting.
  arXiv  Detail & Related papers  (2025-06-08T20:14:48Z)
• DUPRE: Data Utility Prediction for Efficient Data Valuation [49.60564885180563]
  Cooperative game theory-based data valuation, such as Data Shapley, requires evaluating the data utility and retraining the ML model for multiple data subsets.<n>Our framework, textttDUPRE, takes an alternative yet complementary approach that reduces the cost per subset evaluation by predicting data utilities instead of evaluating them by model retraining.<n>Specifically, given the evaluated data utilities of some data subsets, textttDUPRE fits a emphGaussian process (GP) regression model to predict the utility of every other data subset.
  arXiv  Detail & Related papers  (2025-02-22T08:53:39Z)
• Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach [72.19525160912943]
  We first theoretically demonstrate the inherent connection between distribution shift, data perturbation, and model weight perturbation. We then analyze the sufficient conditions to guarantee fairness for the target dataset. Motivated by these sufficient conditions, we propose robust fairness regularization (RFR)
  arXiv  Detail & Related papers  (2023-03-06T17:19:23Z)
• Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
  Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
  arXiv  Detail & Related papers  (2022-01-11T23:01:12Z)
• Fair and efficient contribution valuation for vertical federated learning [19.536358114259887]
  Federated learning is an emerging technology for training machine learning models across decentralized data sources without sharing data.<n>We propose a contribution valuation metric called vertical federated Shapley value (VerFedSV) based on the classic Shapley value.<n>We show that VerFedSV satisfies many desirable properties of fairness but is also efficient to compute.
  arXiv  Detail & Related papers  (2022-01-07T19:57:15Z)
• Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards [26.850070556844628]
  This paper presents a novel collaborative generative modeling (CGM) framework that incentivizes collaboration among self-interested parties to contribute data. Distributing synthetic data as rewards offers task- and model-agnostic benefits for downstream learning tasks.
  arXiv  Detail & Related papers  (2021-12-17T05:15:30Z)
• Fair Densities via Boosting the Sufficient Statistics of Exponential Families [72.34223801798422]
  We introduce a boosting algorithm to pre-process data for fairness. Our approach shifts towards better data fitting while still ensuring a minimal fairness guarantee. Empirical results are present to display the quality of result on real-world data.
  arXiv  Detail & Related papers  (2020-12-01T00:49:17Z)
• A Principled Approach to Data Valuation for Federated Learning [73.19984041333599]
  Federated learning (FL) is a popular technique to train machine learning (ML) models on decentralized data sources. The Shapley value (SV) defines a unique payoff scheme that satisfies many desiderata for a data value notion. This paper proposes a variant of the SV amenable to FL, which we call the federated Shapley value.
  arXiv  Detail & Related papers  (2020-09-14T04:37:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.