Adaptive Federated Dropout: Improving Communication Efficiency and Generalization for Federated Learning

Understand

With more regulations tackling users' privacy-sensitive data protection in recent years, access to such data has become increasingly restricted and controversial.

• To exploit the wealth of data generated and located at distributed entities such as mobile phones, a revolutionary decentralized machine learning setting, known as Federated Learning, enables multiple clients located at different geographical locations to collaboratively learn a machine learning model while keeping all their data on-device.
• However, the scale and decentralization of federated learning present new challenges.
• Communication between the clients and the server is considered a main bottleneck in the convergence time of federated learning.