  Fair machine learning has become a significant research topic with broad
societal impact. However, most fair learning methods require direct access to
personal demographic data, which is increasingly restricted to use for
protecting user privacy (e.g. by the EU General Data Protection Regulation). In
this paper, we propose a distributed fair learning framework for protecting the
privacy of demographic data. We assume this data is privately held by a third
party, which can communicate with the data center (responsible for model
development) without revealing the demographic information. We propose a
principled approach to design fair learning methods under this framework,
exemplify four methods and show they consistently outperform their existing
counterparts in both fairness and accuracy across three real-world data sets.
We theoretically analyze the framework, and prove it can learn models with high
fairness or high accuracy, with their trade-offs balanced by a threshold
variable.
