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

Personalized Federated Learning (PFL) has witnessed remarkable advancements,enabling the development of innovative machine learning applications thatpreserve the privacy of training data. However, existing theoretical researchin this field has primarily focused on distributed optimization forminimization problems. This paper is the first to study PFL for saddle pointproblems encompassing a broader range of optimization problems, that requiremore than just solving minimization problems. In this work, we consider arecently proposed PFL setting with the mixing objective function, an approachcombining the learning of a global model together with locally distributedlearners. Unlike most previous work, which considered only the centralizedsetting, we work in a more general and decentralized setup that allows us todesign and analyze more practical and federated ways to connect devices to thenetwork. We proposed new algorithms to address this problem and provide atheoretical analysis of the smooth (strongly) convex-(strongly) concave saddlepoint problems in stochastic and deterministic cases. Numerical experiments forbilinear problems and neural networks with adversarial noise demonstrate theeffectiveness of the proposed methods.

Decentralized Personalized Federated Learning for Min-Max Problems