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

Many problems in machine learning rely on multi-task learning (MTL), in whichthe goal is to solve multiple related machine learning tasks simultaneously.MTL is particularly relevant for privacy-sensitive applications in areas suchas healthcare, finance, and IoT computing, where sensitive data from multiple,varied sources are shared for the purpose of learning. In this work, weformalize notions of client-level privacy for MTL via joint differentialprivacy (JDP), a relaxation of differential privacy for mechanism design anddistributed optimization. We then propose an algorithm for mean-regularizedMTL, an objective commonly used for applications in personalized federatedlearning, subject to JDP. We analyze our objective and solver, providingcertifiable guarantees on both privacy and utility. Empirically, we find thatour method provides improved privacy/utility trade-offs relative to globalbaselines across common federated learning benchmarks.

Private Multi-Task Learning: Formulation and Applications to Federated Learning