Federated Learning: From Theory to Practice | Paper Citation Record

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Federated Learning: From Theory to Practice Rudin,Real and Complex Analysis, 3rd ed

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This paper cites Rudin,Principles of Mathematical Analysis, 3rd ed.

Federated Learning: From Theory to Practice Rudin,Principles of Mathematical Analysis, 3rd ed

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Federated Learning: From Theory to Practice Unresolved cited work

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Federated Learning: From Theory to Practice An analysis of the total least squares problem,

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Federated Learning: From Theory to Practice The future of industrial communication: Automation networks in the era of the internet of things and industry 4.0,

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Federated Learning: From Theory to Practice The emergence of edge computing,

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Federated Learning: From Theory to Practice Wearable devices for the detection of covid-19,

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Federated Learning: From Theory to Practice The industrial internet of things (iiot): An analysis framework,

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Federated Learning: From Theory to Practice Unresolved cited work

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Federated Learning: From Theory to Practice Network medicine: a network- based approach to human disease,

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This paper cites Communication-Efficient Learning of Deep Networks from Decentralized Data,.

Federated Learning: From Theory to Practice Communication-Efficient Learning of Deep Networks from Decentralized Data,

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This paper cites Federated learning: Challenges, methods, and future directions,.

Federated Learning: From Theory to Practice Federated learning: Challenges, methods, and future directions,

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This paper cites Federated learning for privacy- preserving ai,.

Federated Learning: From Theory to Practice Federated learning for privacy- preserving ai,

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Federated Learning: From Theory to Practice cpSGD: Communication-efficient and differentially-private distributed sgd,

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This paper cites Federated Multi-Task Learning,.

Federated Learning: From Theory to Practice Federated Multi-Task Learning,

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This paper cites Ship compute or ship data? why not both?.

Federated Learning: From Theory to Practice Ship compute or ship data? why not both?

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Federated Learning: From Theory to Practice Unresolved cited work

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Federated Learning: From Theory to Practice van Steen and A

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Federated Learning: From Theory to Practice Tse and P

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This paper cites Applied Federated Learning: Improving Google Keyboard Query Suggestions.

Federated Learning: From Theory to Practice Applied Federated Learning: Improving Google Keyboard Query Suggestions

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This paper cites An efficient frame- work for clustered federated learning,.

Federated Learning: From Theory to Practice An efficient frame- work for clustered federated learning,

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This paper cites Clustered federated learning: Model-agnostic distributed multitask optimization under privacy con- straints,.

Federated Learning: From Theory to Practice Clustered federated learning: Model-agnostic distributed multitask optimization under privacy con- straints,

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Federated Learning: From Theory to Practice Strang,Computational Science and Engineering

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This paper cites Strang,Introduction to Linear Algebra, 5th ed.

Federated Learning: From Theory to Practice Strang,Introduction to Linear Algebra, 5th ed

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Federated Learning: From Theory to Practice Unresolved cited work

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Federated Learning: From Theory to Practice Jung,Machine Learning: The Basics, 1st ed

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Federated Learning: From Theory to Practice Shalev-Shwartz and S

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Federated Learning: From Theory to Practice Can you program ethics into a self-driving car?

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Federated Learning: From Theory to Practice Induction of decision trees,

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Federated Learning: From Theory to Practice Schölkopf and A

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Federated Learning: From Theory to Practice First-order methods for nonsmooth convex large-scale optimization, I: General purpose methods,

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Federated Learning: From Theory to Practice Billingsley,Probability and Measure, 3rd ed

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Federated Learning: From Theory to Practice An RKHS Approach to Estimation with Sparsity Constraints

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Federated Learning: From Theory to Practice Unresolved cited work

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Federated Learning: From Theory to Practice Dermatologist-level classification of skin cancer with deep neural networks,

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Federated Learning: From Theory to Practice Lütkepohl,New Introduction to Multiple Time Series Analysis

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Federated Learning: From Theory to Practice Wainwright,High-Dimensional Statistics: A Non-Asymptotic View- point

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Federated Learning: From Theory to Practice Clustered fed- erated learning via generalized total variation minimization,

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Federated Learning: From Theory to Practice Spectral graph theory,

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Federated Learning: From Theory to Practice Spectral and algebraic graph theory,

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Federated Learning: From Theory to Practice Spectral graph theory,

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Federated Learning: From Theory to Practice Hastie, R

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Federated Learning: From Theory to Practice Beck,First-Order Methods in Optimization

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Federated Learning: From Theory to Practice Proximal algorithms,

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Federated Learning: From Theory to Practice A primal–dual splitting method for convex optimization involving lipschitzian, proximable and linear composite terms,

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Federated Learning: From Theory to Practice An efficient parallel solver for SDD linear systems,

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Federated Learning: From Theory to Practice Lx = b — Laplacian solvers and their algorithmic applications,

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Federated Learning: From Theory to Practice Convex clustering: Model, theoretical guarantee and efficient algorithm,

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Federated Learning: From Theory to Practice Convex clus- tering shrinkage,

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Federated Learning: From Theory to Practice Unresolved cited work

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Federated Learning: From Theory to Practice Boyd and L

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Federated Learning: From Theory to Practice Unresolved cited work

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Federated Learning: From Theory to Practice Locally weighted learning,

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Federated Learning: From Theory to Practice Neuraltangentkernel: Convergence and generalization in neural networks,

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Federated Learning: From Theory to Practice Gradient descent provably optimizes over-parameterized neural networks,

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Federated Learning: From Theory to Practice A comparative analysis of optimization and generalization properties of two-layer neural network and random feature models under gradient descent dynamics,

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Federated Learning: From Theory to Practice Paszke, S

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Federated Learning: From Theory to Practice Unresolved cited work

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Federated Learning: From Theory to Practice No more pesky learning rates,

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Federated Learning: From Theory to Practice Learning to learn by gradi- ent descent by gradient descent,

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Federated Learning: From Theory to Practice Nesterov,Introductory lectures on convex optimization, ser

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Federated Learning: From Theory to Practice Bauschke and P

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Federated Learning: From Theory to Practice Istrăt,escu,Fixed point theory: An Introduction, ser

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Federated Learning: From Theory to Practice Exponential graph is provably efficient for decentralized deep training,

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Federated Learning: From Theory to Practice Fastest mixing markov chain on a graph,

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Federated Learning: From Theory to Practice Internet time synchronization: the network time protocol,

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Federated Learning: From Theory to Practice Hirvonen and J

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Federated Learning: From Theory to Practice Diestel,Graph Theory

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Federated Learning: From Theory to Practice Federated optimization in heterogeneous networks,

Reference 70

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Federated Learning: From Theory to Practice Tanenbaum and D

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Federated Learning: From Theory to Practice Convergence of a block coordinate descent method for nondifferentiable minimization,

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Federated Learning: From Theory to Practice Distributed learning systems with first-order methods,

Reference 74

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Federated Learning: From Theory to Practice Towards efficient scheduling of federated mobile devices under computational and statistical heterogeneity,

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Federated Learning: From Theory to Practice Asynchronous iterations in opti- mization: new sequence results and sharper algorithmic guarantees,

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Federated Learning: From Theory to Practice A primer on monotone operator methods,

Reference 77

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Federated Learning: From Theory to Practice Attack robustness and centrality of complex networks

Reference 79

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Federated Learning: From Theory to Practice An omnibus test for normality for small samples,

Reference 80

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Federated Learning: From Theory to Practice Chapelle, B

Reference 82

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Reference 83

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Federated Learning: From Theory to Practice Ludwig and N

Reference 84

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Federated Learning: From Theory to Practice Personalized federated learning using hypernetworks,

Reference 85

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Federated Learning: From Theory to Practice Learning to compare: Relation network for few-shot learning,

Reference 86

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Federated Learning: From Theory to Practice Few-shot learning with graph neural networks

Reference 87

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Federated Learning: From Theory to Practice Network medicine: a network-based approach to human disease,

Reference 88

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Federated Learning: From Theory to Practice Localized linear regression in networked data,

Reference 89

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Federated Learning: From Theory to Practice Network lasso: Clustering and optimization in large graphs,

Reference 90

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Federated Learning: From Theory to Practice Graphical LASSO Based Model Selection for Time Series,

Reference 91

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Federated Learning: From Theory to Practice Learning the conditional independence structure of stationary time series: A multitask learning approach,

Reference 92

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Federated Learning: From Theory to Practice How to learn a graph from smooth signals,

Reference 93

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Federated Learning: From Theory to Practice Learning graphs from data: A signal representation perspective,

Reference 94

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Federated Learning: From Theory to Practice Nešetřil and P

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Federated Learning: From Theory to Practice An introduction to matrix concentration inequalities,

Reference 96

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Federated Learning: From Theory to Practice Clustering in partially labeled stochastic block models via total variation minimization,

Reference 97

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Federated Learning: From Theory to Practice Bollobas, W

Reference 98

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Federated Learning: From Theory to Practice Keiser,Optical Fiber Communication, 4th ed

Reference 99

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Federated Learning: From Theory to Practice Tse and P

Reference 100

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