A thesis proving communication-acceleration guarantees for local-step, compressed, Byzantine-robust, and low-rank federated optimization methods, assembled from the author's own published papers.
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
We resolve the min-max complexity of distributed stochastic convex optimization (up to a log factor) in the intermittent communication setting, where $M$ machines work in parallel over the course of $R$ rounds of communication to optimize the objective, and during each round of communication, each machine may sequentially compute $K$ stochastic gradient estimates. We present a novel lower bound with a matching upper bound that establishes an optimal algorithm.
fields
cs.LG 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization
A thesis proving communication-acceleration guarantees for local-step, compressed, Byzantine-robust, and low-rank federated optimization methods, assembled from the author's own published papers.