Fairness in Federated Learning via Core-Stability

no code implementations • 3 Nov 2022 • Bhaskar Ray Chaudhury, Linyi Li, Mintong Kang, Bo Li, Ruta Mehta

Nonetheless, the heterogeneity nature of distributed data makes it challenging to define and ensure fairness among local agents.

Decision Making Fairness +1

Certifying Some Distributional Fairness with Subpopulation Decomposition

1 code implementation • 31 May 2022 • Mintong Kang, Linyi Li, Maurice Weber, Yang Liu, Ce Zhang, Bo Li

In this paper, we first formulate the certified fairness of an ML model trained on a given data distribution as an optimization problem based on the model performance loss bound on a fairness constrained distribution, which is within bounded distributional distance with the training distribution.

Fairness

Label-Assemble: Leveraging Multiple Datasets with Partial Labels

2 code implementations • 25 Sep 2021 • Mintong Kang, Yongyi Lu, Alan L. Yuille, Zongwei Zhou

The success of deep learning relies heavily on large and diverse datasets with extensive labels, but we often only have access to several small datasets associated with partial labels.

MgSvF: Multi-Grained Slow vs. Fast Framework for Few-Shot Class-Incremental Learning

no code implementations • 28 Jun 2020 • Hanbin Zhao, Yongjian Fu, Mintong Kang, Qi Tian, Fei Wu, Xi Li

As a challenging problem, few-shot class-incremental learning (FSCIL) continually learns a sequence of tasks, confronting the dilemma between slow forgetting of old knowledge and fast adaptation to new knowledge.

class-incremental learning Incremental Learning