Incentivizing Truthfulness and Collaborative Fairness in Bayesian Learning

2026-05-12 · Rachael Hwee Ling Sim, Jue Fan, Xiao Tian, Xinyi Xu, Patrick Jaillet, Bryan Kian Hsiang Low arxiv

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.

📄 PDF Abstract BibTeX arXiv:2605.11889

Code (0)

등록된 구현이 없습니다.

Stochastically Dominant Peer Prediction

2025-06-02 · Yichi Zhang, Shengwei Xu, David Pennock, Grant Schoenebeck

Eliciting reliable human feedback is essential for many machine learning tasks, such as learning from noisy labels and aligning AI systems with human preferences. Peer prediction mechanisms incentivize truthful reporting…

Fairness Prediction Sensitivity

FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning

2024-05-28 · Zihui Wang, Zheng Wang, Lingjuan Lyu, Zhaopeng Peng 외

Collaborative fairness stands as an essential element in federated learning to encourage client participation by equitably distributing rewards based on individual contributions. Existing methods primarily focus on adjus…

Collaborative Fairness Fairness Federated Learning

TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning

2025-09-30 · Zhepei Wei, Xiao Yang, Kai Sun, Jiaqi Wang 외 arxiv

While large language models (LLMs) have demonstrated strong performance on factoid question answering, they are still prone to hallucination and untruthful responses, particularly when tasks demand information outside th…

General Reinforcement Learning Question Answering

Incentivizing Inclusive Contributions in Model Sharing Markets

2025-05-05 · Enpei Zhang, Jingyi Chai, Rui Ye, Yanfeng Wang 외

While data plays a crucial role in training contemporary AI models, it is acknowledged that valuable public data will be exhausted in a few years, directing the world's attention towards the massive decentralized private…

Federated Learning Instruction Following model Personalized Federated Learning

Blockchain-based Framework for Scalable and Incentivized Federated Learning

2025-02-20 · Bijun Wu, Oshani Seneviratne

Federated Learning (FL) enables collaborative model training without sharing raw data, preserving privacy while harnessing distributed datasets. However, traditional FL systems often rely on centralized aggregating mecha…

Fairness Federated Learning