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Found 6 papers, 0 papers with code
Federated Fairness without Access to Sensitive Groups
Current approaches to group fairness in federated learning assume the existence of predefined and labeled sensitive groups during training.
Minimax Demographic Group Fairness in Federated Learning
Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models.
Federating for Learning Group Fair Models
Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models.
Blind Pareto Fairness and Subgroup Robustness
With the wide adoption of machine learning algorithms across various application domains, there is a growing interest in the fairness properties of such algorithms.
Learning data-derived privacy preserving representations from information metrics
We study space-preserving transformations where the utility provider can use the same algorithm on original and sanitized data, a critical and novel attribute to help service providers accommodate varying privacy requirements with a single set of utility algorithms.
Learning to Collaborate for User-Controlled Privacy
As such, users and utility providers should collaborate in data privacy, a paradigm that has not yet been developed in the privacy research community.
