Authors

* External authors

Venue

Date

Share

Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning

Xu Xinyi*

Lingjuan Lyu

Xingjun Ma*

Chenglin Miao*

Chuan-Sheng Foo*

Bryan Kian Hsiang Low*

* External authors

NeurIPS-2021

2021

Abstract

Collaborative machine learning provides a promising framework for different agents to pool their resources (e.g., data) for a common learning task. In realistic settings where agents are self-interested and not altruistic, they may be unwilling to share data or model without adequate rewards. Furthermore, as the data/model the agents share may differ in quality, designing rewards which are fair to them is important so they do not feel exploited and discouraged from sharing. In this paper, we investigate this problem in gradient-based collaborative machine learning. We propose a novel cosine gradient Shapley to evaluate the agents’ contributions and design commensurate rewards in the form of better models. Compared to existing baselines, our method is more efficient and does not require a validation dataset. We provide theoretical fairness guarantees and empirically validate the effectiveness of our method.

Related Publications

Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception?

NeurIPS, 2023
Xiaoxiao Sun*, Nidham Gazagnadou, Vivek Sharma, Lingjuan Lyu, Hongdong Li*, Liang Zheng*

Hand-crafted image quality metrics, such as PSNR and SSIM, are commonly used to evaluate model privacy risk under reconstruction attacks. Under these metrics, reconstructed images that are determined to resemble the original one generally indicate more privacy leakage. Image…

UltraRE: Enhancing RecEraser for Recommendation Unlearning via Error Decomposition

NeurIPS, 2023
Yuyuan Li*, Chaochao Chen*, Yizhao Zhang*, Weiming Liu*, Lingjuan Lyu, Xiaolin Zheng*, Dan Meng*, Jun Wang*

With growing concerns regarding privacy in machine learning models, regulations have committed to granting individuals the right to be forgotten while mandating companies to develop non-discriminatory machine learning systems, thereby fueling the study of the machine unlearn…

Towards Personalized Federated Learning via Heterogeneous Model Reassembly

NeurIPS, 2023
Jiaqi Wang*, Xingyi Yang*, Suhan Cui*, Liwei Che*, Lingjuan Lyu, Dongkuan Xu*, Fenglong Ma*

This paper focuses on addressing the practical yet challenging problem of model heterogeneity in federated learning, where clients possess models with different network structures. To track this problem, we propose a novel framework called pFedHR, which leverages heterogeneo…

  • HOME
  • Publications
  • Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning

JOIN US

Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.