Optimal gradient quantization reduces communication costs in distributed deep learning.
problem High communication costs in distributed training of deep neural networks.
method Deduced optimal gradient quantization conditions for binary and multi-level quantization, developed novel schemes for dynamic quantization levels.
result Demonstrated superior performance of proposed quantization schemes on CIFAR and ImageNet datasets.
A new method reduces communication in distributed learning by skipping less informative gradient updates.
problem Efficient communication in distributed machine learning.
method Quantizes and skips less informative gradients to reduce communication overhead.
result Proves linear convergence rate similar to gradient descent with significant communication savings.
Paper proposes double quantization to reduce communication in distributed machine learning.
problem High communication overhead in synchronizing stochastic gradients and model parameters in distributed training.
method Proposes double quantization for model parameters and gradients, and three communication-efficient algorithms.
result Established performance guarantees and demonstrated effective bit reduction without performance degradation.
AdaQuantFL reduces communication in federated learning by adaptively quantizing model updates.
problem Efficient communication of model updates in federated learning with high-dimensional models and limited bandwidth.
method AdaQuantFL uses adaptive quantization to reduce the number of bits for model updates while maintaining low error floor.
result AdaQuantFL converges in fewer communicated bits compared to fixed quantization levels, with minimal impact on accuracy.
Moniqua improves SGD convergence with quantized communication.
problem Efficiently communicating in decentralized SGD with limited bandwidth.
method Modulo quantized communication in decentralized SGD.
result Moniqua converges at the same rate as full-precision communication with less bits.
Quantized Epoch-SGD reduces communication in distributed learning.
problem High communication cost in SGD-based distributed learning.
method Quantizes parameters with variance reduction for efficient distributed learning.
result QESGD achieves better performance with less communication compared to other methods.
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.
Quantized Decentralized Gradient Descent (QDGD) solves distributed optimization with quantized communications.
problem Minimizing the sum of smooth and strongly convex functions over a network of distributed agents with quantized communications.
method Proposes QDGD algorithm combining quantized and local information for decentralized gradient descent.
result Achieves vanishing mean solution error under strong convexity and smoothness assumptions.
A new gradient quantization scheme improves communication efficiency in distributed training.
problem Efficiently compressing gradients for parallel training of large models.
method Proposes a new gradient quantization scheme with theoretical guarantees and empirical performance.
result The new scheme matches and exceeds the performance of existing methods.
Quantized Frank-Wolfe reduces communication costs in distributed optimization.
problem Efficiently reducing communication overhead in distributed machine learning optimization.
method Quantized Frank-Wolfe (QFW), a projection-free algorithm for constrained optimization.
result Strong theoretical guarantees on convergence rate, efficient compression of gradients.
Quantized-TinyLLaVA reduces communication costs in split learning for multimodal models.
problem High communication costs in split learning for multimodal models.
method Integrates a compression module that quantizes intermediate features into discrete representations before transmission.
result Achieves an approximate 87.5% reduction in communication overhead with 2-bit quantization.
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.
MQGrad uses reinforcement learning to dynamically adjust gradient quantization bits.
problem Reduction of communication overhead in large-scale machine learning model training.
method Reinforcement learning applied to gradient quantization in parameter server.
result MQGrad accelerates deep neural network learning while maintaining prediction accuracy.
FedPAQ improves federated learning efficiency by averaging and quantizing updates.
problem Communication bottlenecks and scalability issues in federated learning.
method Periodic averaging, partial device participation, and quantized message-passing.
result FedPAQ achieves near-optimal theoretical guarantees and demonstrates communication-computation tradeoffs.
Proposes NUQSGD for efficient parallel training of large models.
problem Efficiently compressing gradients for parallel SGD training.
method Nonuniform quantization scheme for improved theoretical and empirical performance.
result NUQSGD outperforms QSGDinf and other compression methods.
Quantized Adam reduces communication cost in deep learning training.
problem Reducing communication cost in distributed deep learning training.
method Gradient and weight quantization with error feedback in Adam.
result Proposed methods converge to first-order stationary points.
FTTQ optimizes quantized networks in federated learning, reducing communication costs.
problem Redundant parameters in full-precision models lead to excessive communication costs in federated learning.
method FTTQ algorithm that optimizes quantized networks on clients through self-learning quantization factors.
result FTTQ reduces communication costs and can achieve slightly better performance on non-IID data.
vqSGD reduces communication in distributed optimization with convergence guarantees.
problem Reduction of communication cost in distributed optimization.
method Vector quantization schemes based on convex hull of a point set.
result Asymptotic reduction in communication cost with convergence guarantees.
Global-QSGD accelerates distributed training by up to 3.51%.
problem High communication overhead in distributed deep learning.
method Allreduce-compatible gradient quantization with theoretical guarantees.
result Global-QSGD accelerates distributed training by up to 3.51%.
The paper tackles decision-oriented communications for energy-efficient resource allocation.
problem Maximizing utility functions under quantized information.
method Develops solutions for quantizing information to maximize utility functions under known and observed conditions.
result Quantizing the state roughly is optimal for sum-rate maximization but not for energy-efficiency metrics.
Paper shows how to integrate quantization into neural compression models.
problem Integrating quantization into neural compression models.
method Integrates uniform noise channel at test time using universal quantization.
result Eliminates mismatch between training and test phases while maintaining differentiability.
Adaptive quantization improves SGD accuracy in data-parallel settings.
problem Fixed gradient quantization schemes lead to suboptimal performance in deep learning.
method Developed adaptive quantization schemes ALQ and AMQ that update compression schemes based on gradient statistics.
result Improved validation accuracy on CIFAR-10 and ImageNet datasets by 2% and 1% respectively.
Asynchronous decentralized SGD with quantized and local updates converges in gossip model.
problem Scalable distributed machine learning challenges in decentralized optimization.
method Asynchronous decentralized SGD with quantization and local steps in gossip model.
result Decentralized optimization with quantization and local steps converges in asynchronous gossip model.
Study hypothesis testing under quantized samples with communication constraints, achieving near-optimal sample complexity.
problem Optimizing hypothesis testing with quantized samples and communication constraints.
method Developed a polynomial-time algorithm achieving near-optimal sample complexity under communication constraints.
result Achieved near-optimal sample complexity under communication constraints, with a logarithmic factor increase over unconstrained setting.
SPARQ-SGD optimizes communication in decentralized SGD with event-triggered and compressed updates.
problem Efficient communication in decentralized stochastic optimization for large-scale models.
method Event-triggered and compressed algorithm with quantized and sparsified model parameters.
result SPARQ-SGD converges with efficiency comparable to uncompressed training, demonstrating significant communication savings.
Relationships that exist between the classical, Shannon-type, and geometric-based approaches to sampling are investigated. Some aspects of coding and communication through a Gaussian channel are considered. In particular, a constructive method to determine the quantizing dimension in Zador's theorem is provided. A geom…
HSQ reduces communication costs in federated learning.
problem High cost of communicating gradients in federated learning.
method Hyper-sphere quantization (HSQ) framework for efficient gradient compression.
result HSQ achieves O(logd) per-iteration communication cost, significantly reducing costs without compromising accuracy. Differentially quantized gradient methods improve convergence in noisy communication channels.
problem Optimizing distributed learning with limited communication bandwidth and noise.
method Introduces Differential Quantization (DQ) to compensate for quantization errors in gradient descent.
result DQ-GD achieves the same contraction factor as unquantized GD at high bitrates, proving asymptotic optimality.
Distributed quantization improves classification accuracy with less data.
problem Efficiently classify features from distributed nodes with limited communication.
method Designs tailored quantization schemes for classification, proving NP-hardness and proposing polynomial-time algorithms.
result Tailored quantizers can reduce bit communication by more than a factor of two for the same accuracy.
Paper reduces communication in distributed machine learning.
problem Reduces burdensome communication in distributed machine learning.
method Introduces communication-censoring technique to reduce transmissions of variables.
result CSGD algorithm achieves same convergence rate as SGD but with significant communication reduction.
This paper improves federated learning efficiency by auto-tuning secure aggregation parameters.
problem Efficient communication in federated learning with quantized updates.
method Auto-tuning secure aggregation parameters based on random rotation properties.
result Improved communication efficiency in federated learning with secure aggregation.
Researchers show NN-based communication algorithms can be implemented on hardware without significant performance loss.
problem Reducing complexity and improving performance of NN-based communication algorithms for practical hardware implementation.
method Implementation of NN-based algorithms in fixed-point arithmetic with quantized weights on specialized hardware (FPGAs, ASICs).
result It is possible to implement NN-based algorithms in fixed-point arithmetic with quantized weights on hardware without significant performance loss.
Novel algorithm reduces delays and communication in decentralized learning.
problem Decentralized learning with straggling nodes and high communication costs.
method QuanTimed-DSGD: deadline-imposed gradient computation and quantized model exchange.
result Converges to global optimal for convex functions, finds first-order stationary points for non-convex.
Qsparse-local-SGD reduces communication in large-scale learning models.
problem Communication bottleneck in distributed optimization of large-scale models.
method Combines sparsification, quantization, and local computation with error compensation.
result Converges at the same rate as vanilla distributed SGD for many sparsifiers and quantizers.
DFedAvgM is a decentralized FedAvg with momentum for privacy and communication efficiency.
problem Efficiently train models with privacy and communication efficiency in federated learning.
method Decentralized Federated Averaging with Momentum (DFedAvgM) on clients connected by an undirected graph, using stochastic gradient descent with momentum and quantization.
result DFedAvgM converges under trivial assumptions and can be improved with the PŁ property, numerically verified.
We optimize distributed learning algorithms to maintain linear convergence with limited communication.
problem Limited communication time affects the convergence of distributed learning algorithms.
method We design quantizers to compress algorithm information while preserving linear convergence and characterize communication time.
result We show how to co-design machine learning and communication protocols for optimal performance.
Compressed Federated Distillation reduces communication in federated learning.
problem Communication constraints in Federated Learning.
method Compressed Federated Distillation (CFD) leverages soft labels and quantization techniques.
result Reduces communication by more than 4 orders of magnitude compared to Federated Averaging.
Quantized Stochastic Primal-Dual Methods for Distributed Optimization
problem Distributed optimization with stochastic gradients and finite-bit communication
method q-PDGD, a quantized stochastic primal-dual method
result Linear contraction to an explicit neighborhood under RSI, O(1/k) convergence under PL inequality
It is of fundamental importance to find algorithms obtaining optimal performance for learning of statistical models in distributed and communication limited systems. Aiming at characterizing the optimal strategies, we consider learning of Gaussian Processes (GPs) in distributed systems as a pivotal example. We first ad…
GradiVeQ reduces CNN training time by 50% with 5X faster gradient aggregation.
problem Significant communication costs in gradient aggregation for distributed CNN training.
method GradiVeQ uses PCA to vector quantize gradients for direct RAR aggregation.
result GradiVeQ reduces wall-clock gradient aggregation time by more than 5X.
New algorithm reduces communication in distributed learning by sharing compressed beliefs.
problem Efficiently learning from private data in a distributed setting with large hypothesis sets.
method Proposes a belief update rule for distributed cooperative learning with compressed (sparse or quantized) beliefs.
result Beliefs converge almost surely to optimal hypotheses with a linear concentration rate.
The paper explores distributed learning with minimal communication in high-dimensional settings.
problem Learning in high-dimensional settings with sublinear communication.
method Combining mirror descent with randomized sparsification/quantization of iterates.
result It is possible to learn a d-dimensional model in the distributed setting with logarithmic communication in d. Improved SGD reduces communication overhead by sparsifying gradients.
problem Communication overhead in distributed optimization.
method Top-k sparsification and error compensation.
result Communication can be reduced by a factor of the problem dimension.
SQuARM-SGD improves decentralized SGD efficiency with momentum.
problem Efficient decentralized training of large-scale models over networks.
method Fixed local SGD steps with Nesterov's momentum, sparsified and quantized updates, locally computed triggering criterion.
result Convergence rate matches vanilla SGD, momentum improves test performance.
Decentralized detection avoids sharing data, controls false discoveries.
problem Global false discovery rate control in decentralized novelty detection.
method Quantized surrogate models for low-precision sharing, preserving exchangeability.
result Quantized composite scores maintain competitive statistical power with reduced communication.
Distributed sensors compress and send features to a fusion center for linear regression.
problem Efficiently compress and transmit features from distributed sensors to a fusion center under varying communication constraints.
method Designs a distributed and adaptive feature compression scheme using optimal quantizers and simple adaptive strategies.
result Demonstrates improved inference performance through simulated experiments.
FedNew improves federated learning efficiency and privacy.
problem Low communication efficiency and privacy issues in Newton-type methods for federated learning.
method Introduces a two-level framework using ADMM for inverse Hessian-gradient approximation and Newton's method for global model updates, reducing communication overhead.
result FedNew achieves superior communication efficiency and privacy compared to existing methods.
Deep learning compresses and quantizes log-likelihood ratios for fading channels.
problem Efficiently compress and quantize log-likelihood ratios for fading channels.
method Trains a deep autoencoder network to map log-likelihood ratios to a latent space and reconstruct them.
result Achieves a compression factor of nearly three times with minimal performance loss.