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

To lower the communication complexity of federated min-max learning, anatural approach is to utilize the idea of infrequent communications (throughmultiple local updates) same as in conventional federated learning. However,due to the more complicated inter-outer problem structure in federated min-maxlearning, theoretical understandings of communication complexity for federatedmin-max learning with infrequent communications remain very limited in theliterature. This is particularly true for settings with non-i.i.d. datasets andpartial client participation. To address this challenge, in this paper, wepropose a new algorithmic framework called stochastic sampling averaginggradient descent ascent (SAGDA), which i) assembles stochastic gradientestimators from randomly sampled clients as control variates and ii) leveragestwo learning rates on both server and client sides. We show that SAGDA achievesa linear speedup in terms of both the number of clients and local update steps,which yields an $\mathcal{O}(\epsilon^{-2})$ communication complexity that isorders of magnitude lower than the state of the art. Interestingly, by notingthat the standard federated stochastic gradient descent ascent (FSGDA) is infact a control-variate-free special version of SAGDA, we immediately arrive atan $\mathcal{O}(\epsilon^{-2})$ communication complexity result for FSGDA.Therefore, through the lens of SAGDA, we also advance the current understandingon communication complexity of the standard FSGDA method for federated min-maxlearning.

SAGDA: Achieving $\mathcal{O}(\epsilon^{-2})$ Communication Complexity in Federated Min-Max Learning