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 is a privacy-preserving training method which consists oftraining from a plurality of clients but without sharing their confidentialdata. However, previous work on federated learning do not explore suitableneural network architectures for clients with different input images sizes anddifferent numbers of output categories. In this paper, we propose an effectivefederated learning method named ScalableFL, where the depths and widths of thelocal models for each client are adjusted according to the clients' input imagesize and the numbers of output categories. In addition, we provide a new boundfor the generalization gap of federated learning. In particular, this boundhelps to explain the effectiveness of our scalable neural network approach. Wedemonstrate the effectiveness of ScalableFL in several heterogeneous clientsettings for both image classification and object detection tasks.

Scalable Federated Learning for Clients with Different Input Image Sizes and Numbers of Output Categories