  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 shared 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. Hence, we propose a novel boosting
algorithm for FL to address both the generalization and gradient leakage
issues, as well as achieve faster convergence 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 and avoid pairwise encryption that is not scalable. We
demonstrate the proposed Federated Boosting (FedBoosting) method achieves
noticeable improvements in both prediction accuracy and run-time efficiency in
a visual text recognition task on public benchmark.
