Pith. sign in

REVIEW 3 cited by

Distributed learning with compressed gradients

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1806.06573 v2 pith:CHWFBCIV submitted 2018-06-18 math.OC stat.ML

Sarit Khirirat , Hamid Reza Feyzmahdavian , Mikael Johansson This is my paper · ORCID
classification math.OCstat.ML
keywords compressionconvergencedistributedgradientalgorithmsboundscompressedexchange
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Asynchronous computation and gradient compression have emerged as two key techniques for achieving scalability in distributed optimization for large-scale machine learning. This paper presents a unified analysis framework for distributed gradient methods operating with staled and compressed gradients. Non-asymptotic bounds on convergence rates and information exchange are derived for several optimization algorithms. These bounds give explicit expressions for step-sizes and characterize how the amount of asynchrony and the compression accuracy affect iteration and communication complexity guarantees. Numerical results highlight convergence properties of different gradient compression algorithms and confirm that fast convergence under limited information exchange is indeed possible.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Theoretical Analysis on how Learning Rate Warmup Accelerates Convergence

    cs.LG 2025-09 conditional novelty 7.0 of 10

    Under a new smoothness assumption linking local curvature to the loss gap, increasing learning rates provably accelerate GD and SGD convergence, with up to Theta(T) speedup in special cases.

  2. Delayed Momentum Aggregation: Communication-efficient Byzantine-robust Federated Learning with Partial Participation

    cs.LG 2025-09 conditional novelty 6.0 of 10

    D-Byz-SGDM aggregates cached momentum from non-sampled clients together with fresh momentum from sampled clients, preserving Byzantine robustness under partial participation and achieving an optimal O(cδζ²/p) stationa...

  3. The Ball-Proximal (="Broximal") Point Method: a New Algorithm, Convergence Theory, and Applications

    math.OC 2025-02 conditional novelty 5.0 of 10

    A ball-constrained minimization oracle yields an idealized optimization method with finite and linear convergence for nonsmooth convex problems, plus a tailored 'ball-convex' nonconvex class.

Pith tools