Hierarchically Fair Federated Learning

Traditional machine learning techniques require agents (e.g., mobile devices, terminals, companies, etc.) to upload their data to a central server. This approach not only increases communication between agents and the central server due to the data volume but also entails privacy risks during data transfer or due to a server breach [1]. This is an important concern since data protection regulations impose constraints on sharing of sensitive data. Federated learning, a recent distributed and decentralized machine learning scheme [2] has attracted significant attention. In federated learning, agents maintain their data locally and collaboratively learn a global machine learning model that benefits all. Specifically, each agent sends parameters (or parameters update) of local models to the central server and receives the computed parameters of the global model from the central server. In this way, all agents can jointly train a global model without exposing their own data. This scheme has desirable properties such as privacy-preservation, efficient communication, and decentralized data storage.