Federated Learning: Strategies for Improving Communication Efficiency

September 2019

tl;dr: Seminal paper on federated learning: distributed machine learning approach which enables model training on a large corpus of decentralized data.

Overall impression

FL solves the problem of data privacy (critical for hospitals, financial institutes, etc).

In FL, the training data is kept locally on users’ mobile devices, and the devices are used as nodes performing computation on their local data in order to update a global model.

FL has its own challenge compared to distributed machine learning, due to the data imbalance, non iid data and large number of devices under unreliable connection.

Key ideas

Technical details