No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices

Understand

Federated learning (FL) is an important paradigm for training global models from decentralized data in a privacy-preserving way.

• Existing FL methods usually assume the global model can be trained on any participating client.
• However, in real applications, the devices of clients are usually heterogeneous, and have different computing power.
• Although big models like BERT have achieved huge success in AI, it is difficult to apply them to heterogeneous FL with weak clients.