FedBoost: Federated Learning with Gradient Protected Boosting for Text Recognition

Typical machine learning approaches require centralized data for model training, which may not be possible where restrictions on data sharing are in place due to, for instance, privacy and gradient protection. The recently proposed Federated Learning (FL) framework allows learning a shared model collaboratively without data being centralized or data sharing among data owners. However, we show in this paper that the generalization ability of the joint model is poor on Non-Independent and Non-Identically Distributed (Non-IID) data, particularly when the Federated Averaging (FedAvg) strategy is used due to the weight divergence phenomenon. We propose a novel boosting algorithm for FL to address this generalization issue, as well as achieving a much faster convergence rate in gradient-based optimization. In addition, a secure gradient sharing protocol using Homomorphic Encryption (HE) and Differential Privacy (DP) is introduced to defend against gradient leakage attack. We demonstrate the proposed Federated Boosting (FedBoost) method achieves significant improvements in both prediction accuracy and run-time efficiency on text recognition task using public benchmark.