mlsanityData to 14 Oct 2026

Towards Federated Long-Tailed Learning

arXiv 2206.14988 ↗First posted 30 Jun 2022cs.LG cs.AI cs.DCcited by 7

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Abstract (arXiv)

Data privacy and class imbalance are the norm rather than the exception in many machine learning tasks. Recent attempts have been launched to, on one side, address the problem of learning from pervasive private data, and on the other side, learn from long-tailed data. However, both assumptions might hold in practical applications, while an effective method to simultaneously alleviate both issues is yet under development. In this paper, we focus on learning with long-tailed (LT) data distributions under the context of the popular privacy-preserved federated learning (FL) framework. We characterize three scenarios with different local or global long-tailed data distributions in the FL framework, and highlight the corresponding challenges. The preliminary results under different scenarios reveal that substantial future work are of high necessity to better resolve the characterized federated long-tailed learning tasks.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 27 of its 35 references that are in the map. First posted on arXiv 30 Jun 2022.

Builds on

27 of 35 references.

References · 27

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2019LVIS: A Dataset for Large Vocabulary Instance Segmentationcited by 1,217
  4. 2019Learning Imbalanced Datasets with Label-Distribution-Aware Margin Losscited by 989
  5. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  6. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  7. 2019Towards Federated Learning at Scale: System Designcited by 832
  8. 2019Decoupling Representation and Classifier for Long-Tailed Recognitioncited by 809
  9. 2019Large-Scale Long-Tailed Recognition in an Open Worldcited by 705
  10. 2017Federated Multi-Task Learningcited by 672
  11. 2021Federated Learning Based on Dynamic Regularizationcited by 562
  12. 2019Federated Learning with Personalization Layerscited by 541
15 more
  1. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  2. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  3. 2021Towards Personalized Federated Learningcited by 464
  4. 2021Deep Long-Tailed Learning: A Surveycited by 417
  5. 2019BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognitioncited by 403
  6. 2020Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrievalcited by 309
  7. 2020Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effectcited by 256
  8. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 198
  9. 2020Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasetscited by 122
  10. 2020Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspectivecited by 120
  11. 2020The Devil is in Classification: A Simple Framework for Long-tail Object Detection and Instance Segmentationcited by 108
  12. 2021FedMix: Approximation of Mixup under Mean Augmented Federated Learningcited by 108
  13. 2019Dynamic Curriculum Learning for Imbalanced Data Classificationcited by 93
  14. 2022Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Featurescited by 63
  15. 2020Siloed Federated Learning for Multi-Centric Histopathology Datasetscited by 61

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 4; 2025: 0; 2026: 1.

7 papers.

  1. 2023Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancercited by 17
  2. 2024FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Datacited by 10
  3. 2024Spectral Co-Distillation for Personalized Federated Learningcited by 8
  4. 2023Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classificationcited by 4
  5. 2024Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalizationcited by 2
  6. 2024The Key of Parameter Skew in Federated Learningcited by 0
  7. 2026Towards Federated Long-Tailed Graph Learning: An Energy-Guided Dual Decoupling Approachcited by 0