Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments

We introduce a novel federated learning framework, FedD3, which reduces the overall communication volume and with that opens up the concept of federated learning to more application scenarios in network-constrained environments. It achieves this by leveraging local dataset distillation instead of traditional learning approaches (i) to significantly reduce communication volumes and (ii) to limit transfers to one-shot communication, rather than iterative multiway communication. Instead of sharing model updates, as in other federated learning approaches, FedD3 allows the connected clients to distill the local datasets independently, and then aggregates those decentralized distilled datasets (typically in the form a few unrecognizable images, which are normally smaller than a model) across the network only once to form the final model. Our experimental results show that FedD3 significantly outperforms other federated learning frameworks in terms of needed communication volumes, while it provides the additional benefit to be able to balance the trade-off between accuracy and communication cost, depending on usage scenario or target dataset. For instance, for training an AlexNet model on a Non-IID CIFAR-10 dataset with 10 clients, FedD3 can either increase the accuracy by over 71% with a similar communication volume, or save 98% of communication volume, while reaching the same accuracy, comparing to other one-shot federated learning approaches.