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

Federated Learning (FL) is a collaborative learning framework that enablesedge devices to collaboratively learn a global model while keeping raw datalocally. Although FL avoids leaking direct information from local datasets,sensitive information can still be inferred from the shared models. To addressthe privacy issue in FL, differential privacy (DP) mechanisms are leveraged toprovide formal privacy guarantee. However, when deploying FL at the wirelessedge with over-the-air computation, ensuring client-level DP faces significantchallenges. In this paper, we propose a novel wireless FL scheme called privatefederated edge learning with sparsification (PFELS) to provide client-level DPguarantee with intrinsic channel noise while reducing communication and energyoverhead and improving model accuracy. The key idea of PFELS is for each deviceto first compress its model update and then adaptively design the transmitpower of the compressed model update according to the wireless channel statuswithout any artificial noise addition. We provide a privacy analysis for PFELSand prove the convergence of PFELS under general non-convex and non-IIDsettings. Experimental results show that compared with prior work, PFELS canimprove the accuracy with the same DP guarantee and save communication andenergy costs simultaneously.

Communication and Energy Efficient Wireless Federated Learning with Intrinsic Privacy