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 promising technology via which some edgedevices/clients collaboratively train a machine learning model orchestrated bya server. Learning an unfair model is known as a critical problem in federatedlearning, where the trained model may unfairly advantage or disadvantage someof the devices. To tackle this problem, in this work, we propose AdaFed. Thegoal of AdaFed is to find an updating direction for the server along which (i)all the clients' loss functions are decreasing; and (ii) more importantly, theloss functions for the clients with larger values decrease with a higher rate.AdaFed adaptively tunes this common direction based on the values of localgradients and loss functions. We validate the effectiveness of AdaFed on asuite of federated datasets, and demonstrate that AdaFed outperformsstate-of-the-art fair FL methods.

AdaFed: Fair Federated Learning via Adaptive Common Descent Direction