Federated Learning with Matched Averaging

Understand

Federated learning allows edge devices to collaboratively learn a shared model while keeping the training data on device, decoupling the ability to do model training from the need to store the data in the cloud.

• We propose Federated matched averaging (FedMA) algorithm designed for federated learning of modern neural network architectures e.g.
• convolutional neural networks (CNNs) and LSTMs.
• FedMA constructs the shared global model in a layer-wise manner by matching and averaging hidden elements (i.e.