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

Federated learning (FL) aims to train machine learning models in thedecentralized system consisting of an enormous amount of smart edge devices.Federated averaging (FedAvg), the fundamental algorithm in FL settings,proposes on-device training and model aggregation to avoid the potential heavycommunication costs and privacy concerns brought by transmitting raw data.However, through theoretical analysis we argue that 1) the multiple steps oflocal updating will result in gradient biases and 2) there is an inconsistencybetween the expected target distribution and the optimization objectivesfollowing the training paradigm in FedAvg. To tackle these problems, we firstpropose an unbiased gradient aggregation algorithm with the keep-trace gradientdescent and the gradient evaluation strategy. Then we introduce an additionalcontrollable meta updating procedure with a small set of data samples,indicating the expected target distribution, to provide a clear and consistentoptimization objective. Both the two improvements are model- and task-agnosticand can be applied individually or together. Experimental results demonstratethat the proposed methods are faster in convergence and achieve higher accuracywith different network architectures in various FL settings.

Federated Learning with Unbiased Gradient Aggregation and Controllable Meta Updating