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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Nowadays, Deep Neural Networks are widely applied to various domains.However, massive data collection required for deep neural network reveals thepotential privacy issues and also consumes large mounts of communicationbandwidth. To address these problems, we propose a privacy-preserving methodfor the federated learning distributed system, operated on Intel Software GuardExtensions, a set of instructions that increase the security of applicationcode and data. Meanwhile, the encrypted models make the transmission overheadlarger. Hence, we reduce the commutation cost by sparsification and it canachieve reasonable accuracy with different model architectures.

ESMFL: Efficient and Secure Models for Federated Learning