mlsanityData to 7 Oct 2026

Aggregation Delayed Federated Learning

arXiv 2108.07433 ↗First posted 17 Aug 2021cs.LG cs.DCcited by 2

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Abstract (arXiv)

Federated learning is a distributed machine learning paradigm where multiple data owners (clients) collaboratively train one machine learning model while keeping data on their own devices. The heterogeneity of client datasets is one of the most important challenges of federated learning algorithms. Studies have found performance reduction with standard federated algorithms, such as FedAvg, on non-IID data. Many existing works on handling non-IID data adopt the same aggregation framework as FedAvg and focus on improving model updates either on the server side or on clients. In this work, we tackle this challenge in a different view by introducing redistribution rounds that delay the aggregation. We perform experiments on multiple tasks and show that the proposed framework significantly improves the performance on non-IID data.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 28 of its 39 references that are in the map. First posted on arXiv 17 Aug 2021.

Builds on

28 of 39 references.

References · 28

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  5. 2018Federated Learning with Non-IID Datacited by 1,099
  6. 2020Adaptive Federated Optimizationcited by 1,036
  7. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  8. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  9. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
  10. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  11. 2017Ray: A Distributed Framework for Emerging AI Applicationscited by 516
  12. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 454
16 more
  1. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
  2. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  3. 2018Not All Samples Are Created Equal: Deep Learning with Importance Samplingcited by 323
  4. 2019Asynchronous Federated Optimizationcited by 264
  5. 2018Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Datacited by 242
  6. 2019Federated Learning for Emoji Prediction in a Mobile Keyboardcited by 115
  7. 2019Robust Federated Learning in a Heterogeneous Environmentcited by 88
  8. 2019Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Recordscited by 70
  9. 2019Federated Learning Of Out-Of-Vocabulary Wordscited by 51
  10. 2018Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Datacited by 46
  11. 2019Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Datacited by 38
  12. 2021Local-Global Knowledge Distillation in Heterogeneous Federated Learning with Non-IID Datacited by 31
  13. 2020Federated Learning under Importance Samplingcited by 26
  14. 2019Two-stage Federated Phenotyping and Patient Representation Learningcited by 19
  15. 2019Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensingcited by 11
  16. 2019Gradient Scheduling with Global Momentum for Non-IID Data Distributed Asynchronous Trainingcited by 5

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 1.

2 papers.

  1. 2022Auxo: Efficient Federated Learning via Scalable Client Clusteringcited by 8
  2. 2024Federated Automated Feature Engineeringcited by 1