What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness | Paper Citation Record

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This paper cites The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research

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This paper cites Byzantine stochastic gradient descent.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Byzantine stochastic gradient descent

Reference 2

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This paper cites The convergence of sparsified gradient methods.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness The convergence of sparsified gradient methods

Reference 3

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This paper cites Is federated learning still alive in the foundation model era? In AAAI Spring Symposium, 2024.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Is federated learning still alive in the foundation model era? In AAAI Spring Symposium, 2024

Reference 4

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This paper cites Generative ai has an intellectual property problem.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Generative ai has an intellectual property problem

Reference 5

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This paper cites Designing for privacy - wwdc19 - videos, 2019.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Designing for privacy - wwdc19 - videos, 2019

Reference 6

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This paper cites Communication complexity of distributed convex learning and optimization.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Communication complexity of distributed convex learning and optimization

Reference 7

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This paper cites Lower Bounds for Non-Convex Stochastic Optimization.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Lower Bounds for Non-Convex Stochastic Optimization

Reference 8

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Observation 85a1fea0-e969-4e3b-9855-852cd98e3636 · outbound

This paper cites Self-concordant analysis for logistic regression.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Self-concordant analysis for logistic regression

Reference 9

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Observation 9fd9afd6-9a89-4148-a310-7034a0d5a2a6 · outbound

This paper cites Implicit gradient alignment in distributed and federated learning.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Implicit gradient alignment in distributed and federated learning

Reference 10

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This paper cites A model of inductive bias learning.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness A model of inductive bias learning

Reference 11

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This paper cites On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency , pages 610–623, 2021.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency , pages 610–623, 2021

Reference 12

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This paper cites The Compute Divide in Machine Learning: A Threat to Academic Contribution and Scrutiny?.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness The Compute Divide in Machine Learning: A Threat to Academic Contribution and Scrutiny?

Reference 13

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This paper cites Collaborative pac learning.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Collaborative pac learning

Reference 14

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Observation dff21a1d-6c83-48b1-a839-0b7120b7eafb · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness On the Opportunities and Risks of Foundation Models

Reference 15

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This paper cites Reinforcement learning, efficient coding, and the statistics of natural tasks.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Reinforcement learning, efficient coding, and the statistics of natural tasks

Reference 16

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This paper cites The computational and neural basis of cognitive control: charted territory and new frontiers.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness The computational and neural basis of cognitive control: charted territory and new frontiers

Reference 17

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This paper cites Language Models are Few-Shot Learners.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Language Models are Few-Shot Learners

Reference 18

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Observation 6c86788d-0dce-4190-815b-d66c184c2b87 · outbound

This paper cites Regret analysis of stochastic and nonstochastic multi- armed bandit problems.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Regret analysis of stochastic and nonstochastic multi- armed bandit problems

Reference 19

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Observation d7832a07-2979-47d3-822f-90c532d6e30c · outbound

This paper cites Convex optimization: Algorithms and complexity.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Convex optimization: Algorithms and complexity

Reference 20

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This paper cites Highly smooth minimization of non-smooth problems.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Highly smooth minimization of non-smooth problems

Reference 21

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This paper cites A stochastic newton algorithm for distributed convex optimization.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness A stochastic newton algorithm for distributed convex optimization

Reference 22

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This paper cites Lower bounds for finding stationary points i.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Lower bounds for finding stationary points i

Reference 23

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This paper cites Acceleration with a ball optimization oracle.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Acceleration with a ball optimization oracle

Reference 24

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Multitask learning

Reference 25

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Observation ecb9c6f0-ff13-4fa2-921c-ae816c4bd3de · outbound

This paper cites On the Outsized Importance of Learning Rates in Local Update Methods.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness On the Outsized Importance of Learning Rates in Local Update Methods

Reference 26

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This paper cites On large- cohort training for federated learning.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness On large- cohort training for federated learning

Reference 27

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This paper cites Federated Learning Of Out-Of-Vocabulary Words.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Federated Learning Of Out-Of-Vocabulary Words

Reference 28

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This paper cites Fl-qsar: a federated learning-based qsar prototype for collaborative drug discovery.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Fl-qsar: a federated learning-based qsar prototype for collaborative drug discovery

Reference 29

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This paper cites Opportunities and obstacles for deep learning in biology and medicine.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Opportunities and obstacles for deep learning in biology and medicine

Reference 30

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Observation 5db00176-4452-40a1-b19c-9c59aa30e3d3 · outbound

This paper cites Machine learning needs big data to revolutionise drug discovery.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Machine learning needs big data to revolutionise drug discovery

Reference 31

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This paper cites Cognitive control over learning: creating, clustering, and generalizing task-set structure.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Cognitive control over learning: creating, clustering, and generalizing task-set structure

Reference 32

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Observation 628b4914-c62b-454d-af93-3b3d7049aa50 · outbound

This paper cites Momentum-based variance reduction in non-convex sgd.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Momentum-based variance reduction in non-convex sgd

Reference 33

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This paper cites Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learning.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learning

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Observation 995293ec-ccac-4369-a4a1-27b81fa34fb6 · outbound

This paper cites Federated learning for predicting clinical outcomes in patients with covid-19.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Federated learning for predicting clinical outcomes in patients with covid-19

Reference 35

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Observation 61f91b74-18c6-4a52-835f-914b77ebf6bc · outbound

This paper cites Optimal distributed online prediction using mini-batches.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Optimal distributed online prediction using mini-batches

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Observation 512bc2d4-90e6-4931-9b95-b8856f0c94b1 · outbound

This paper cites Communication trade-offs for local-sgd with large step size.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Communication trade-offs for local-sgd with large step size

Reference 37

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Observation 348bf42b-c5d3-476b-9db5-dff994a68d1c · outbound

This paper cites Differentially-private federated linear bandits.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Differentially-private federated linear bandits

Reference 38

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This paper cites Optimal rates for zero- order convex optimization: The power of two function evaluations.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Optimal rates for zero- order convex optimization: The power of two function evaluations

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This paper cites The multiple-demand (md) system of the primate brain: mental programs for intelligent behaviour.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness The multiple-demand (md) system of the primate brain: mental programs for intelligent behaviour

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This paper cites Federated Learning in Vehicular Networks.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Federated Learning in Vehicular Networks

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Unresolved cited work

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Observation 96ee1066-8550-4173-bc5f-e62a664c5617 · outbound

This paper cites Spider: Near-optimal non-convex op- timization via stochastic path-integrated differential estimator.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Spider: Near-optimal non-convex op- timization via stochastic path-integrated differential estimator

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This paper cites Model-agnostic meta-learning for fast adaptation of deep networks.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Model-agnostic meta-learning for fast adaptation of deep networks

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Observation 0ad7b1f2-9c89-43b7-b40e-cdc93f30d5c3 · outbound

This paper cites Online convex optimization in the bandit setting: gradient descent without a gradient.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Online convex optimization in the bandit setting: gradient descent without a gradient

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Observation 80b889de-d9b8-4847-a4a0-a0143230f6c8 · outbound

This paper cites EControl: Fast distributed optimization with compression and error control.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness EControl: Fast distributed optimization with compression and error control

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Observation 2d6c6ed8-b8a3-44a7-99d6-977ae2b62cf2 · outbound

This paper cites Resource-aware asynchronous online federated learning for nonlinear regression.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Resource-aware asynchronous online federated learning for nonlinear regression

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Observation b223f6f3-16f6-4e00-a7eb-7df266d71801 · outbound

This paper cites Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization i: A generic algorithmic framework.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization i: A generic algorithmic framework

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Ai and memory wall

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This paper cites Sharp bounds for federated averaging (local sgd) and continuous perspective.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Sharp bounds for federated averaging (local sgd) and continuous perspective

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This paper cites Communication- efficient online federated learning framework for nonlinear regression.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Communication- efficient online federated learning framework for nonlinear regression

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Unresolved cited work

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Observation 7d0da0de-54cd-4a05-a2b3-4917284a99b8 · outbound

This paper cites Your voice amp; audio data stays private while google assistant improves, 2023.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Your voice amp; audio data stays private while google assistant improves, 2023

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Resolution: verified exact; raw_fallback, observed 2026-08-06T21:27:28.730054Z

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Observation 97ab9bb8-81bb-452a-a074-0cb052d5cedf · outbound

This paper cites Why (and When) does Local SGD Generalize Better than SGD?.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Why (and When) does Local SGD Generalize Better than SGD?

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Resolution: unresolved; no resolver link, observed 2026-08-06T21:27:22.750921Z

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Observation f35a2cc0-921e-41d3-9a43-5e8fef69fe8e · outbound

This paper cites On-demand sampling: Learning optimally from multiple distributions.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness On-demand sampling: Learning optimally from multiple distributions

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Resolution: unresolved; no resolver link, observed 2026-08-06T21:27:22.832114Z

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Observation 8c3601c6-6e48-4904-9ac1-fdb6126b0b94 · outbound

This paper cites On the Effect of Defections in Federated Learning and How to Prevent Them.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness On the Effect of Defections in Federated Learning and How to Prevent Them

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Resolution: verified exact; local_arxiv, observed 2026-08-06T21:27:28.666848Z

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Observation caea3301-5036-4380-968e-87f87b109d75 · outbound

This paper cites How apple personalizes siri without hoovering up your data.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness How apple personalizes siri without hoovering up your data

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Observation 7bcdd7f3-b51c-40eb-a400-eba51ec1daec · outbound

This paper cites Federated Learning for Mobile Keyboard Prediction.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Federated Learning for Mobile Keyboard Prediction

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Observation 9c5e1511-23e6-496e-9888-2f8c586aa9be · outbound

This paper cites Predicting text selections with federated learning, Nov 2021.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Predicting text selections with federated learning, Nov 2021

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Observation c93cf39c-7ee7-424b-aa2f-d3ec9591a814 · outbound

This paper cites Introduction to online convex optimization.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Introduction to online convex optimization

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This paper cites A simple and provably efficient algorithm for asynchronous federated contextual linear bandits.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness A simple and provably efficient algorithm for asynchronous federated contextual linear bandits

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Federated linear contextual bandits

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Stabilized proximal-point methods for federated optimization

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Federated optimization with doubly reg- ularized drift correction

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Advances and Open Problems in Federated Learning

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This paper cites End-to-end privacy preserving deep learning on multi-institutional medical imaging.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness End-to-end privacy preserving deep learning on multi-institutional medical imaging

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Functional specificity in the human brain: a window into the functional architecture of the mind

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Scaling Laws for Neural Language Models

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Error feedback fixes signsgd and other gradient compression schemes

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Scaffold: Stochastic controlled averaging for federated learning

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Learning from history for byzantine robust optimization

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Tighter theory for local sgd on identical and heterogeneous data

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness A payload optimization method for federated recommender systems

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Stem: A stochastic two-sided momentum algorithm achieving near-optimal sample and com- munication complexities for federated learning

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Observation 0241cde1-f714-4b05-8b5c-7f8c9ec257e1 · outbound

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness A unified theory of decentralized sgd with changing topology and local updates

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Federated Optimization: Distributed Machine Learning for On-Device Intelligence

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Optimal gradient sliding and its application to optimal distributed optimization under similarity

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Learning multiple layers of features from tiny images.Citeseer, 2009

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Imagenet classification with deep convolu- tional neural networks

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Real time kernel learning for sensor networks using principles of federated learning

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Asynchronous upper confidence bound algorithms for federated linear bandits

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Privacy-preserving federated brain tumour segmentation

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Fedrec++: Lossless federated recommendation with explicit feedback

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Analyzing implicit regularization in federated learning, 2024

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Don't Use Large Mini-Batches, Use Local SGD

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Threats, attacks and defenses to federated learning: issues, taxonomy and perspectives

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Revisiting the last-iterate convergence of stochastic gradient methods

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness High probability convergence of stochastic gradient methods

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Lohn and Micah Musser

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness On maintaining linear convergence of distributed learning and optimization under limited communication

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness From local sgd to local fixed-point methods for federated learning

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Observation cbf4d5b2-e657-4718-ac5d-ca9ded5754ea · outbound

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Efficient large- scale distributed training of conditional maximum entropy models

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Federated learning: Collaborative machine learn- ing without centralized training data, 4 2017

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Observation 87bc278e-95f8-4011-b2b2-928328d5e9d1 · outbound

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Communication-Efficient Learning of Deep Networks from Decentralized Data

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Steps toward artificial intelligence

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Resolution: unresolved; no resolver link, observed 2026-08-06T21:27:26.973129Z

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This paper cites Proxskip: Yes! local gradient steps provably lead to communication acceleration! finally! In International Conference on Machine Learning , pages 15750–15769.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Proxskip: Yes! local gradient steps provably lead to communication acceleration! finally! In International Conference on Machine Learning , pages 15750–15769

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Online federated learning

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