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Found 13 papers, 3 papers with code
Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices
In this paper, we propose the first federated GNN framework called Lumos that supports supervised and unsupervised learning with feature and degree protection on node-level federated graphs.
Revealing Unfair Models by Mining Interpretable Evidence
The key idea is to find solid evidence in the form of a group of data instances discriminated most by the model.
Mining Minority-class Examples With Uncertainty Estimates
In the real world, the frequency of occurrence of objects is naturally skewed forming long-tail class distributions, which results in poor performance on the statistically rare classes.
Achieving Model Fairness in Vertical Federated Learning
To solve it in a federated and privacy-preserving manner, we consider the equivalent dual form of the problem and develop an asynchronous gradient coordinate-descent ascent algorithm, where some active data parties perform multiple parallelized local updates per communication round to effectively reduce the number of communication rounds.
FedFair: Training Fair Models In Cross-Silo Federated Learning
In this paper, we first propose a federated estimation method to accurately estimate the fairness of a model without infringing the data privacy of any party.
Finding Representative Interpretations on Convolutional Neural Networks
Interpreting the decision logic behind effective deep convolutional neural networks (CNN) on images complements the success of deep learning models.
Robust Counterfactual Explanations on Graph Neural Networks
Massive deployment of Graph Neural Networks (GNNs) in high-stake applications generates a strong demand for explanations that are robust to noise and align well with human intuition.
Model Complexity of Deep Learning: A Survey
Model complexity is a fundamental problem in deep learning.
Comprehensible Counterfactual Explanation on Kolmogorov-Smirnov Test
One challenge remained untouched is how we can obtain an explanation on why a test set fails the KS test.
Personalized Cross-Silo Federated Learning on Non-IID Data
Non-IID data present a tough challenge for federated learning.
Exact and Consistent Interpretation of Piecewise Linear Models Hidden behind APIs: A Closed Form Solution
More and more AI services are provided through APIs on cloud where predictive models are hidden behind APIs.
Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution
Strong intelligent machines powered by deep neural networks are increasingly deployed as black boxes to make decisions in risk-sensitive domains, such as finance and medical.
Finding Theme Communities from Database Networks
Given a database network where each vertex is associated with a transaction database, we are interested in finding theme communities.
