Practical One-Shot Federated Learning for Cross-Silo Setting

Abstract

Federated learning enables multiple parties to collaboratively learn a model without exchanging their data. While most existing federated learning algorithms need many rounds to converge, one-shot federated learning (i.e., federated learning with a single communication round) is a promising approach to make federated learning applicable in cross-silo setting in practice. However, existing one-shot algorithms only support specific models and do not provide any privacy guarantees, which significantly limit the applications in practice. In this paper, we propose a practical one-shot federated learning algorithm named FedKT. By utilizing the knowledge transfer technique, FedKT can be applied to any classification models and can flexibly achieve differential privacy guarantees. Our experiments on various tasks show that FedKT can significantly outperform the other state-of-the-art federated learning algorithms with a single communication round.

References in corpus (7)

• Distilling the Knowledge in a Neural Network
• Ensemble Distillation for Robust Model Fusion in Federated Learning
• FedMD: Heterogenous Federated Learning via Model Distillation
• One-Shot Federated Learning
• Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer
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Cited by in corpus (3)

• Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge
• Federated Learning on Non-IID Data: A Survey
• Towards Model Agnostic Federated Learning Using Knowledge Distillation