RingFed: Reducing Communication Costs in Federated Learning on Non-IID Data
This addresses inefficiency in federated learning for applications with limited bandwidth, though it is incremental as it modifies the communication topology rather than introducing a new paradigm.
The paper tackles the high communication costs in federated learning by proposing RingFed, a framework that transmits parameters between clients in a ring instead of to a central server, resulting in reduced communication overhead while maintaining fast convergence and high model accuracy on non-IID data.
Federated learning is a widely used distributed deep learning framework that protects the privacy of each client by exchanging model parameters rather than raw data. However, federated learning suffers from high communication costs, as a considerable number of model parameters need to be transmitted many times during the training process, making the approach inefficient, especially when the communication network bandwidth is limited. This article proposes RingFed, a novel framework to reduce communication overhead during the training process of federated learning. Rather than transmitting parameters between the center server and each client, as in original federated learning, in the proposed RingFed, the updated parameters are transmitted between each client in turn, and only the final result is transmitted to the central server, thereby reducing the communication overhead substantially. After several local updates, clients first send their parameters to another proximal client, not to the center server directly, to preaggregate. Experiments on two different public datasets show that RingFed has fast convergence, high model accuracy, and low communication cost.