Communication-Efficient Federated Learning via Quantized Compressed Sensing

• URL: http://arxiv.org/abs/2111.15071v1
• Date: Tue, 30 Nov 2021 02:13:54 GMT
• Title: Communication-Efficient Federated Learning via Quantized Compressed Sensing
• Authors: Yongjeong Oh, Namyoon Lee, Yo-Seb Jeon, and H. Vincent Poor
• Abstract summary: The presented framework consists of gradient compression for wireless devices and gradient reconstruction for a parameter server. Thanks to gradient sparsification and quantization, our strategy can achieve a higher compression ratio than one-bit gradient compression. We demonstrate that the framework achieves almost identical performance with the case that performs no compression.
• Score: 82.10695943017907
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present a communication-efficient federated learning framework inspired by quantized compressed sensing. The presented framework consists of gradient compression for wireless devices and gradient reconstruction for a parameter server (PS). Our strategy for gradient compression is to sequentially perform block sparsification, dimensional reduction, and quantization. Thanks to gradient sparsification and quantization, our strategy can achieve a higher compression ratio than one-bit gradient compression. For accurate aggregation of the local gradients from the compressed signals at the PS, we put forth an approximate minimum mean square error (MMSE) approach for gradient reconstruction using the expectation-maximization generalized-approximate-message-passing (EM-GAMP) algorithm. Assuming Bernoulli Gaussian-mixture prior, this algorithm iteratively updates the posterior mean and variance of local gradients from the compressed signals. We also present a low-complexity approach for the gradient reconstruction. In this approach, we use the Bussgang theorem to aggregate local gradients from the compressed signals, then compute an approximate MMSE estimate of the aggregated gradient using the EM-GAMP algorithm. We also provide a convergence rate analysis of the presented framework. Using the MNIST dataset, we demonstrate that the presented framework achieves almost identical performance with the case that performs no compression, while significantly reducing communication overhead for federated learning.

Related papers

• Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models [66.1595537904019]
  Large language models (LLMs) can act as gradient priors in a zero-shot setting. We introduce LM-GC, a novel method that integrates LLMs with arithmetic coding.
  arXiv  Detail & Related papers  (2024-09-26T13:38:33Z)
• Flattened one-bit stochastic gradient descent: compressed distributed optimization with controlled variance [55.01966743652196]
  We propose a novel algorithm for distributed gradient descent (SGD) with compressed gradient communication in the parameter-server framework. Our gradient compression technique, named flattened one-bit gradient descent (FO-SGD), relies on two simple algorithmic ideas.
  arXiv  Detail & Related papers  (2024-05-17T21:17:27Z)
• Efficient Wireless Federated Learning via Low-Rank Gradient Factorization [34.51800744978221]
  A new protocol termed over-the-air low-rank compression (Ota-LC) is shown to have lower computation cost and lower communication overhead. As an example, when targeting a test accuracy of 70% on the Cifar-10 dataset, Ota-LC reduces total communication costs by at least 33% compared to benchmark schemes.
  arXiv  Detail & Related papers  (2024-01-15T06:30:06Z)
• Scaling Forward Gradient With Local Losses [117.22685584919756]
  Forward learning is a biologically plausible alternative to backprop for learning deep neural networks. We show that it is possible to substantially reduce the variance of the forward gradient by applying perturbations to activations rather than weights. Our approach matches backprop on MNIST and CIFAR-10 and significantly outperforms previously proposed backprop-free algorithms on ImageNet.
  arXiv  Detail & Related papers  (2022-10-07T03:52:27Z)
• Federated Optimization Algorithms with Random Reshuffling and Gradient Compression [2.7554288121906296]
  We provide the first analysis of methods with gradient compression and without-replacement sampling. We show how to reduce the variance coming from gradient quantization through the use of control iterates. We outline several settings in which they improve upon existing algorithms.
  arXiv  Detail & Related papers  (2022-06-14T17:36:47Z)
• Communication-Efficient Distributed SGD with Compressed Sensing [24.33697801661053]
  We consider large scale distributed optimization over a set of edge devices connected to a central server. Inspired by recent advances in federated learning, we propose a distributed gradient descent (SGD) type algorithm that exploits the sparsity of the gradient, when possible, to reduce communication burden. We conduct theoretical analysis on the convergence of our algorithm in the presence of noise perturbation incurred by the communication channels, and also conduct numerical experiments to corroborate its effectiveness.
  arXiv  Detail & Related papers  (2021-12-15T02:10:45Z)
• Wyner-Ziv Gradient Compression for Federated Learning [4.619828919345114]
  Gradient compression is an effective method to reduce communication load by transmitting compressed gradients. This paper proposes a practical gradient compression scheme for federated learning, which uses historical gradients to compress gradients. We also implement our gradient quantization method on the real dataset, and the performance of our method is better than the previous schemes.
  arXiv  Detail & Related papers  (2021-11-16T07:55:43Z)
• An Efficient Statistical-based Gradient Compression Technique for Distributed Training Systems [77.88178159830905]
  Sparsity-Inducing Distribution-based Compression (SIDCo) is a threshold-based sparsification scheme that enjoys similar threshold estimation quality to deep gradient compression (DGC) Our evaluation shows SIDCo speeds up training by up to 41:7%, 7:6%, and 1:9% compared to the no-compression baseline, Topk, and DGC compressors, respectively.
  arXiv  Detail & Related papers  (2021-01-26T13:06:00Z)
• Sparse Communication for Training Deep Networks [56.441077560085475]
  Synchronous gradient descent (SGD) is the most common method used for distributed training of deep learning models. In this algorithm, each worker shares its local gradients with others and updates the parameters using the average gradients of all workers. We study several compression schemes and identify how three key parameters affect the performance.
  arXiv  Detail & Related papers  (2020-09-19T17:28:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.