We consider multi-agent networks that aim at solving, cooperatively and online, distributed optimization problems under communication constraints. We propose the ACTC (Adapt-Compress-Then-Combine) diffusion strategy, which leverages differential randomized compression to infuse the classical ATC strategy with the ability to handle compressed data. We consider the flexible setting of directed graphs and left-stochastic policies, and require strong convexity only at a network level (i.e., some agents might even have non-convex risks). We prove that each agent is able to learn the optimal solution up to a small error on the order of the step-size, achieving remarkable savings in terms of bits exchanged between neighboring agents.

ADAPTIVE DIFFUSION WITH COMPRESSED COMMUNICATION

Carpentiero M.;Matta V.;
2022

Abstract

We consider multi-agent networks that aim at solving, cooperatively and online, distributed optimization problems under communication constraints. We propose the ACTC (Adapt-Compress-Then-Combine) diffusion strategy, which leverages differential randomized compression to infuse the classical ATC strategy with the ability to handle compressed data. We consider the flexible setting of directed graphs and left-stochastic policies, and require strong convexity only at a network level (i.e., some agents might even have non-convex risks). We prove that each agent is able to learn the optimal solution up to a small error on the order of the step-size, achieving remarkable savings in terms of bits exchanged between neighboring agents.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4806192
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? ND
social impact