FedCD: Improving Performance in non-IID Federated Learning

• URL: http://arxiv.org/abs/2006.09637v3
• Date: Mon, 27 Jul 2020 04:55:44 GMT
• Title: FedCD: Improving Performance in non-IID Federated Learning
• Authors: Kavya Kopparapu, Eric Lin, Jessica Zhao
• Abstract summary: Federated learning has been widely applied to enable decentralized devices, which each have their own local data, to learn a shared model. We present a novel approach, FedCD, which clones and deletes models to dynamically group devices with similar data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning has been widely applied to enable decentralized devices, which each have their own local data, to learn a shared model. However, learning from real-world data can be challenging, as it is rarely identically and independently distributed (IID) across edge devices (a key assumption for current high-performing and low-bandwidth algorithms). We present a novel approach, FedCD, which clones and deletes models to dynamically group devices with similar data. Experiments on the CIFAR-10 dataset show that FedCD achieves higher accuracy and faster convergence compared to a FedAvg baseline on non-IID data while incurring minimal computation, communication, and storage overheads.

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