Over-the-Air Federated Learning from Heterogeneous Data

Understand

Federated learning (FL) is a framework for distributed learning of centralized models.

• In FL, a set of edge devices train a model using their local data, while repeatedly exchanging their trained updates with a central server.
• This procedure allows tuning a centralized model in a distributed fashion without having the users share their possibly private data.
• In this paper, we focus on over-the-air (OTA) FL, which has been suggested recently to reduce the communication overhead of FL due to the repeated transmissions of the model updates by a large number of users over the wireless channel.