Federated Learning with Matched Averaging
This addresses the problem of efficient and effective model training in federated learning for edge devices, representing a novel method for a known bottleneck.
The paper tackled federated learning for modern neural networks by proposing the FedMA algorithm, which constructs global models layer-wise by matching and averaging hidden elements with similar signatures, resulting in outperforming state-of-the-art algorithms and reducing communication burden.
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. channels for convolution layers; hidden states for LSTM; neurons for fully connected layers) with similar feature extraction signatures. Our experiments indicate that FedMA not only outperforms popular state-of-the-art federated learning algorithms on deep CNN and LSTM architectures trained on real world datasets, but also reduces the overall communication burden.