Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization | Paper Citation Record

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Deep learning with differential privacy

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Sparse Communication for Distributed Gradient Descent

Reference 2

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Fedrolex: Model-heterogeneous federated learning with rolling sub-model extraction

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Optimal Gradient Compression for Distributed and Federated Learning

Reference 4

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This paper cites Alistarh, D.

Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Alistarh, D

Reference 5

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Geo-indistinguishability: Differential privacy for location-based systems

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Federated Learning with Personalization Layers

Reference 7

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization A tight convergence analysis for stochastic gradient descent with delayed updates

Reference 8

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Stochastic (approximate) proximal point methods: Convergence, optimality, and adaptivity

Reference 9

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Minibatch stochastic approximate proximal point methods

Reference 10

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Attouch and J

Reference 11

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Unresolved cited work

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Unresolved cited work

Reference 13

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Private empirical risk minimization: Efficient algorithms and tight error bounds

Reference 14

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Unresolved cited work

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Deep rewiring: Training very sparse deep networks

Reference 16

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Incremental proximal methods for large scale convex optimization

Reference 17

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization On Biased Compression for Distributed Learning

Reference 18

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization On biased compression for distributed learning

Reference 19

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This paper cites An Evaluation of GPU Filters for Accelerating the 2D Convex Hull.

Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization An Evaluation of GPU Filters for Accelerating the 2D Convex Hull

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This paper cites Towards Federated Learning at Scale: System Design.

Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Towards Federated Learning at Scale: System Design

Reference 21

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Language models are few-shot learners

Reference 22

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Quasi-Newton methods and their application to function minimisation

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Federated User Representation Learning

Reference 24

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Efficient implementation of stochastic proximal point algorithm for matrix and tensor completion

Reference 25

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization LEAF: A Benchmark for Federated Settings

Reference 26

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Accelerated, optimal and parallel: Some results on model-based stochastic optimization

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Chang and C.-J

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Broadening the scope of differential privacy using metrics

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Efficient Personalized Federated Learning via Sparse Model-Adaptation

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Visualgpt: Data-efficient adaptation of pretrained language models for image captioning

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Unresolved cited work

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization A comprehensive survey on model compression and acceleration

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization PaLM: Scaling Language Modeling with Pathways

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 35

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 36

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Emnist: Extending mnist to handwritten letters

Reference 37

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Condat and P

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Condat and P

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Condat, I

Reference 40

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Condat, D

Reference 41

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Condat, G

Reference 42

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization TAMUNA: Doubly Accelerated Distributed Optimization under Partial Participation

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Only tails matter: Average-case universality and robustness in the convex regime

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Large scale distributed deep networks

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Heterofl: Computation and communication efficient federated learning for heterogeneous clients

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Differentially private and communication efficient collaborative learning

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Unresolved cited work

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization The Llama 3 Herd of Models

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Resist: Layer-wise decomposition of resnets for distributed training

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Efficient and light-weight federated learning via asynchronous distributed dropout

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Calibrating noise to sensitivity in private data analysis

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization The algorithmic foundations of differential privacy

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Extreme compression of large language models via additive quantization

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Cizsl++: Creativity inspired generative zero-shot learning

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Rigging the lottery: Making all tickets winners

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization EF21 with Bells & Whistles: Six Algorithmic Extensions of Modern Error Feedback

Reference 58

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Private stochastic convex optimization: optimal rates in linear time

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Fletcher

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Reference 61

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Sparsegpt: Massive language models can be accurately pruned in one-shot

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization OPTQ : Accurate quantization for generative pre-trained transformers

Reference 64

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization vqSGD: Vector Quantized Stochastic Gradient Descent

Reference 65

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization A Survey on Heterogeneous Federated Learning

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization A framework for few-shot language model evaluation

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Feddc: Federated learning with non-iid data via local drift decoupling and correction

Reference 68

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Gasanov, A

Reference 69

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Stochastic first-and zeroth-order methods for nonconvex stochastic programming

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization An efficient framework for clustered federated learning

Reference 71

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization A family of variable-metric methods derived by variational means

Reference 72

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Gorbunov, F

Reference 73

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Gorbunov, F

Reference 74

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Gorbunov, D

Reference 75

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Super-acceleration with cyclical step-sizes

Reference 76

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Unresolved cited work

Reference 77

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Unresolved cited work

Reference 78

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Unresolved cited work

Reference 79

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Sgd: General analysis and improved rates

Reference 80

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Grudzie \'n , G

Reference 81

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization On the Convergence of Local Descent Methods in Federated Learning

Reference 82

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Learning both weights and connections for efficient neural network

Reference 83

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Federated Learning of a Mixture of Global and Local Models

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization One Method to Rule Them All: Variance Reduction for Data, Parameters and Many New Methods

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization A damped newton method achieves global o(1/k^2) and local quadratic convergence rate

Reference 87

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Federated Learning for Mobile Keyboard Prediction

Reference 88

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Fednas: Federated deep learning via neural architecture search

Reference 89

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Deep residual learning for image recognition

Reference 90

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Methods of conjugate gradients for solving linear systems , volume 49

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks

Reference 92

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Horv\'ath , D

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Natural Compression for Distributed Deep Learning

Reference 94

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout

Reference 95

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Distributed Pruning Towards Tiny Neural Networks in Federated Learning

Reference 96

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Privacy-preserving Learning via Deep Net Pruning

Reference 97

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Quantized neural networks: Training neural networks with low precision weights and activations

Reference 98

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Towards practical differentially private convex optimization

Reference 99

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Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Communication-efficient distributed dual coordinate ascent

Reference 100

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