English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Typical machine learning approaches require centralized data for modeltraining, which may not be possible where restrictions on data sharing are inplace due to, for instance, privacy and gradient protection. The recentlyproposed Federated Learning (FL) framework allows learning a shared modelcollaboratively without data being centralized or shared among data owners.However, we show in this paper that the generalization ability of the jointmodel is poor on Non-Independent and Non-Identically Distributed (Non-IID)data, particularly when the Federated Averaging (FedAvg) strategy is used dueto the weight divergence phenomenon. Hence, we propose a novel boostingalgorithm for FL to address both the generalization and gradient leakageissues, 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 gradientleakage attack and avoid pairwise encryption that is not scalable. Wedemonstrate the proposed Federated Boosting (FedBoosting) method achievesnoticeable improvements in both prediction accuracy and run-time efficiency ina visual text recognition task on public benchmark.

FedBoosting: Federated Learning with Gradient Protected Boosting for Text Recognition