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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Due to its advantages in resource constraint scenarios, Split FederatedLearning (SFL) is promising in AIoT systems. However, due to data heterogeneityand stragglers, SFL suffers from the challenges of low inference accuracy andlow efficiency. To address these issues, this paper presents a novel SFLapproach, named Sliding Split Federated Learning (S$^2$FL), which adopts anadaptive sliding model split strategy and a data balance-based trainingmechanism. By dynamically dispatching different model portions to AIoT devicesaccording to their computing capability, S$^2$FL can alleviate the low trainingefficiency caused by stragglers. By combining features uploaded by devices withdifferent data distributions to generate multiple larger batches with a uniformdistribution for back-propagation, S$^2$FL can alleviate the performancedegradation caused by data heterogeneity. Experimental results demonstratethat, compared to conventional SFL, S$^2$FL can achieve up to 16.5\% inferenceaccuracy improvement and 3.54X training acceleration.

Have Your Cake and Eat It Too: Toward Efficient and Accurate Split Federated Learning