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

The emergence of foundation models, including language and vision models, hasreshaped AI's landscape, offering capabilities across various applications.Deploying and fine-tuning these large models, like GPT-3 and BERT, presentschallenges, especially in the current foundation model era. We introduceEmulator-Assisted Tuning (EAT) combined with Parameter-Efficient Fine-Tuning(PEFT) to form Parameter-Efficient Emulator-Assisted Tuning (PEAT). Further, weexpand this into federated learning as Federated PEAT (FedPEAT). FedPEAT usesadapters, emulators, and PEFT for federated model tuning, enhancing modelprivacy and memory efficiency. Adapters adjust pre-trained models, whileemulators give a compact representation of original models, addressing bothprivacy and efficiency. Adaptable to various neural networks, our approach alsouses deep reinforcement learning for hyper-parameter optimization. We testedFedPEAT in a unique scenario with a server participating in collaborativefederated tuning, showcasing its potential in tackling foundation modelchallenges.

FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine  Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation  Models with Mobile Edge Computing