We propose a new optimization formulation for training federated learning models. The
standard formulation has the form of an empirical risk minimization problem constructed to …

In this work, we consider the optimization formulation of personalized federated learning
recently introduced by Hanzely & Richtarik (2020) which was shown to give an alternative …

In this paper, we propose a new accelerated stochastic first-order method called clipped-
SSTM for smooth convex stochastic optimization with heavy-tailed distributed noise in …

We study dual-based algorithms for distributed convex optimization problems over networks,
where the objective is to minimize a sum Σ i= 1 mfi (z) of functions over in a network. We …

Gradient-free/zeroth-order methods for black-box convex optimization have been
extensively studied in the last decade with the main focus on oracle calls complexity. In this …

We study structured convex optimization problems, with additive objective $ r:= p+ q $,
where $ r $ is ($\mu $-strongly) convex, $ q $ is $ L_q $-smooth and convex, and $ p $ is …

We consider distributed convex-concave saddle point problems over arbitrary connected
undirected networks and propose a decentralized distributed algorithm for their solution. The …

We introduce primal and dual stochastic gradient oracle methods for decentralized convex
optimization problems. Both for primal and dual oracles, the proposed methods are optimal …

In the last few years, the theory of decentralized distributed convex optimization has made
significant progress. The lower bounds on communications rounds and oracle calls have …

Consider a convex optimization problem min x∈ Q⊆ Rd f (x)(1) with convex feasible set Q
and convex objective f possessing the zeroth-order (gradient/derivativefree) oracle [83]. The …