On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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5 results for “GADMM”

Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learningcs.LG

Q-GADMM reduces communication in decentralized ML by quantizing model updates.

problem Reducing communication in decentralized ML while maintaining accuracy.

method Quantized group ADMM (Q-GADMM) with adaptive quantization.

result Q-GADMM achieves similar accuracy and convergence to non-quantized methods with less communication.

decentralized machine learning quantization ADMM communication efficiency sparsity

2019-10-23abs ↗pdf ↗

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learningcs.LG

GADMM reduces communication costs in distributed machine learning.

problem Efficiently solving distributed machine learning problems with reduced communication costs.

method Group Alternating Direction Method of Multipliers (GADMM) framework.

result GADMM converges to the optimal solution for convex loss functions and is faster and more communication-efficient than state-of-the-art algorithms.

distributed machine learning communication efficiency ADMM decentralized GADMM

2019-08-30abs ↗pdf ↗

Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMMcs.LG

Efficient decentralized learning framework reduces communication costs.

problem Efficiently solve optimization problems in distributed learning networks.

method Censored and Quantized Generalized GADMM (CQ-GGADMM) framework.

result Achieves linear convergence rate under strong convexity assumptions.

distributed learning decentralized optimization communication efficiency quantization censoring

2020-09-14abs ↗pdf ↗

L-FGADMM: Layer-Wise Federated Group ADMM for Communication Efficient Decentralized Deep Learningcs.LG

A new decentralized deep learning method reduces communication costs without sacrificing accuracy.

problem Efficient decentralized deep learning with reduced communication costs.

method Layer-wise federated group ADMM (L-FGADMM) with adjusted communication periods for different layers.

result By skipping the largest layer consensus, L-FGADMM achieves similar test accuracy to federated learning with significantly reduced communication cost.

decentralized learning federated learning communication efficiency deep neural networks ADMM

2019-11-09abs ↗pdf ↗

Distributed Linear Regression with Compositional Covariatesstat.ML

Paper tackles distributed linear regression with compositional covariates.

problem Solving distributed statistical methodology and computing for massive compositional data.

method Proposes two distributed optimization techniques based on ADMM and CDMM for solving constrained convex optimization problems.

result Established convergence theories for the proposed algorithms under regularity conditions.

distributed linear regression compositional covariates ADMM CDMM constrained optimization

2023-10-21abs ↗pdf ↗