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Found 5 papers, 3 papers with code
Bias Propagation in Federated Learning
Our work calls for auditing group fairness in federated learning and designing learning algorithms that are robust to bias propagation.
On The Impact of Machine Learning Randomness on Group Fairness
We investigate the impact on group fairness of different sources of randomness in training neural networks.
On the Privacy Risks of Algorithmic Fairness
We show that fairness comes at the cost of privacy, and this cost is not distributed equally: the information leakage of fair models increases significantly on the unprivileged subgroups, which are the ones for whom we need fair learning.
On Adversarial Bias and the Robustness of Fair Machine Learning
Optimizing prediction accuracy can come at the expense of fairness.
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer
Collaborative (federated) learning enables multiple parties to train a model without sharing their private data, but through repeated sharing of the parameters of their local models.
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