The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning

Modern data aggregation often involves a platform collecting data from a network of users with various privacy options. Platforms must solve the problem of how to allocate incentives to users to convince them to share their data. This paper puts forth an idea for a \textit{fair} amount to compensate users for their data at a given privacy level based on an axiomatic definition of fairness, along the lines of the celebrated Shapley value. To the best of our knowledge, these …

aggregation constraints cs.cr cs.gt cs.lg data data aggregation fair federated learning incentives modern network paper platform platforms privacy solve them value