mlsanityData to 7 Oct 2026

FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clients

arXiv 2201.01601 ↗First posted 5 Jan 2022cs.LGcited by 46

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Abstract (arXiv)

Federated Learning (FL) trains a machine learning model on distributed clients without exposing individual data. Unlike centralized training that is usually based on carefully-organized data, FL deals with on-device data that are often unfiltered and imbalanced. As a result, conventional FL training protocol that treats all data equally leads to a waste of local computational resources and slows down the global learning process. To this end, we propose FedBalancer, a systematic FL framework that actively selects clients' training samples. Our sample selection strategy prioritizes more "informative" data while respecting privacy and computational capabilities of clients. To better utilize the sample selection to speed up global training, we further introduce an adaptive deadline control scheme that predicts the optimal deadline for each round with varying client training data. Compared with existing FL algorithms with deadline configuration methods, our evaluation on five datasets from three different domains shows that FedBalancer improves the time-to-accuracy performance by 1.20~4.48x while improving the model accuracy by 1.1~5.0%. We also show that FedBalancer is readily applicable to other FL approaches by demonstrating that FedBalancer improves the convergence speed and accuracy when operating jointly with three different FL algorithms.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). Builds on 44 of its 84 references that are in the map. First posted on arXiv 5 Jan 2022.

Builds on

44 of 84 references.

References · 44

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  4. 2014Deep Learning Face Attributes in the Wildcited by 3,679
  5. 2019Advances and Open Problems in Federated Learningcited by 3,021
  6. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  7. 2016Deep Learning with Differential Privacycited by 2,682
  8. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  9. 2018Federated Learning with Non-IID Datacited by 1,099
  10. 2015Prioritized Experience Replaycited by 1,070
  11. 2019Towards Federated Learning at Scale: System Designcited by 826
  12. 2017Deep Bayesian Active Learning with Image Datacited by 812
32 more
  1. 2018LEAF: A Benchmark for Federated Settingscited by 780
  2. 2020Learning from Noisy Labels with Deep Neural Networks: A Surveycited by 580
  3. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  4. 2020Federated Learning with Matched Averagingcited by 564
  5. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  6. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  7. 2019BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learningcited by 428
  8. 2019Fair Resource Allocation in Federated Learningcited by 389
  9. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  10. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 334
  11. 2018Not All Samples Are Created Equal: Deep Learning with Importance Samplingcited by 323
  12. 2020CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognitioncited by 320
  13. 2019On The Power of Curriculum Learning in Training Deep Networkscited by 285
  14. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
  15. 2017Automated Curriculum Learning for Neural Networkscited by 251
  16. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 202
  17. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 196
  18. 2021FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropoutcited by 192
  19. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  20. 2015Online Batch Selection for Faster Training of Neural Networkscited by 121
  21. 2021Online Coreset Selection for Rehearsal-based Continual Learningcited by 114
  22. 2015Variance Reduction in SGD by Distributed Importance Samplingcited by 79
  23. 2020FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoringcited by 61
  24. 2021Sample Selection for Fair and Robust Trainingcited by 48
  25. 2020Bandit-based Communication-Efficient Client Selection Strategies for Federated Learningcited by 29
  26. 2021Memory-aware curriculum federated learning for breast cancer classificationcited by 20
  27. 2019A Smoother Way to Train Structured Prediction Modelscited by 9
  28. 2020Federated Learning for Localization: A Privacy-Preserving Crowdsourcing Methodcited by 9
  29. 2020Optimal Task Assignment to Heterogeneous Federated Learning Devicescited by 7
  30. 2021In-network Computation for Large-scale Federated Learning over Wireless Edge Networkscited by 7
  31. 2021On the Impact of Device and Behavioral Heterogeneity in Federated Learningcited by 4
  32. 2021DistFL: Distribution-aware Federated Learning for Mobile Scenarioscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 12; 2024: 13; 2025: 11; 2026: 6.

46 papers.

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  1. 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
  2. 2024Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Modelscited by 49
  3. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
  4. 2025HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Modelscited by 30
  5. 2023FwdLLM: Efficient FedLLM using Forward Gradientcited by 25
  6. 2022FedAdapter: Efficient Federated Learning for Modern NLPcited by 24
  7. 2023FS-Real: Towards Real-World Cross-Device Federated Learningcited by 22
  8. 2024FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extractioncited by 21
  9. 2022Federated Few-Shot Learning for Mobile NLPcited by 15
  10. 2023DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Datasetcited by 14
  11. 2023MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulationcited by 13
  12. 2023FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learningcited by 10
34 more
  1. 2024Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Studycited by 10
  2. 2024Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight Generationcited by 9
  3. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  4. 2022To Store or Not? Online Data Selection for Federated Learning with Limited Storagecited by 7
  5. 2023Flame: Simplifying Topology Extension in Federated Learningcited by 7
  6. 2025FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clientscited by 7
  7. 2024Delta: A Cloud-assisted Data Enrichment Framework for On-Device Continual Learningcited by 6
  8. 2023Mitigating Group Bias in Federated Learning for Heterogeneous Devicescited by 5
  9. 2024Improving Local Training in Federated Learning via Temperature Scalingcited by 5
  10. 2024(FL)2: Overcoming Few Labels in Federated Semi-Supervised Learningcited by 5
  11. 2024Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splittingcited by 4
  12. 2024LIFL: A Lightweight, Event-driven Serverless Platform for Federated Learningcited by 2
  13. 2026Learning to Collaborate: An Orchestrated-Decentralized Framework for Peer-to-Peer LLM Federationcited by 2
  14. 2023DynamicFL: Balancing Communication Dynamics and Client Manipulation for Federated Learningcited by 1
  15. 2023Federated Learning in IoT: a Survey from a Resource-Constrained Perspectivecited by 1
  16. 2023EcoLearn: Optimizing the Carbon Footprint of Federated Learningcited by 1
  17. 2025FedHybrid: Breaking the Memory Wall of Federated Learning via Hybrid Tensor Managementcited by 1
  18. 2023Knowledge Rumination for Client Utility Evaluation in Heterogeneous Federated Learningcited by 0
  19. 2024Agent-oriented Joint Decision Support for Data Owners in Auction-based Federated Learningcited by 0
  20. 2024FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selectioncited by 0
  21. 2024Where is the Testbed for my Federated Learning Research?cited by 0
  22. 2025Smoothing ADMM for Non-convex and Non-smooth Hierarchical Federated Learningcited by 0
  23. 2025Moss: Proxy Model-based Full-Weight Aggregation in Federated Learning with Heterogeneous Modelscited by 0
  24. 2025ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and Reasoningcited by 0
  25. 2025Toward Storage-Aware Learning with Compressed Data An Empirical Exploratory Study on JPEGcited by 0
  26. 2025PracMHBench: Re-evaluating Model-Heterogeneous Federated Learning Based on Practical Edge Device Constraintscited by 0
  27. 2025PQFed: A Privacy-Preserving Quality-Controlled Federated Learning Frameworkcited by 0
  28. 2025FLAMMABLE: A Multi-Model Federated Learning Framework with Multi-Model Engagement and Adaptive Batch Sizescited by 0
  29. 2025Federated Learning Framework for Scalable AI in Heterogeneous HPC and Cloud Environmentscited by 0
  30. 2026Representation-Aligned Multi-Scale Personalization for Federated Learningcited by 0
  31. 2026Sample Selection Using Multi-Task Autoencoders in Federated Learning with Non-IID Datacited by 0
  32. 2026Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selectioncited by 0
  33. 2026Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learningcited by 0
  34. 2026Latency-Aware Client Assignment for Parallel Split Learning With Global Samplingcited by 0