mlsanityData to 7 Oct 2026

Resource-Efficient Federated Learning

arXiv 2111.01108 ↗First posted 1 Nov 2021cs.LG cs.DCcited by 26

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Abstract (arXiv)

Federated Learning (FL) enables distributed training by learners using local data, thereby enhancing privacy and reducing communication. However, it presents numerous challenges relating to the heterogeneity of the data distribution, device capabilities, and participant availability as deployments scale, which can impact both model convergence and bias. Existing FL schemes use random participant selection to improve fairness; however, this can result in inefficient use of resources and lower quality training. In this work, we systematically address the question of resource efficiency in FL, showing the benefits of intelligent participant selection, and incorporation of updates from straggling participants. We demonstrate how these factors enable resource efficiency while also improving trained model quality.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 46 of its 91 references that are in the map. First posted on arXiv 1 Nov 2021.

Builds on

46 of 91 references.

References · 46

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
  6. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  7. 2017ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devicescited by 1,194
  8. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  9. 2019Towards Federated Learning at Scale: System Designcited by 826
  10. 2018LEAF: A Benchmark for Federated Settingscited by 780
  11. 2018How To Backdoor Federated Learningcited by 745
  12. 2018Speech Commands: A Dataset for Limited-Vocabulary Speech Recognitioncited by 741
34 more
  1. 2017Federated Multi-Task Learningcited by 669
  2. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  3. 2018The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scalecited by 542
  4. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 509
  5. 2019Agnostic Federated Learningcited by 451
  6. 2019Fair Resource Allocation in Federated Learningcited by 389
  7. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 349
  8. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  9. 2019Asynchronous Federated Optimizationcited by 264
  10. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
  11. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 202
  12. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  13. 2017Revisiting Distributed Synchronous SGDcited by 142
  14. 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 135
  15. 2020Federated Visual Classification with Real-World Data Distributioncited by 126
  16. 2020Optimal Client Sampling for Federated Learningcited by 123
  17. 2018A generic framework for privacy preserving deep learningcited by 103
  18. 2019Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregationcited by 99
  19. 2019SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overheadcited by 82
  20. 2018Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGDcited by 76
  21. 2020Training Production Language Models without Memorizing User Datacited by 68
  22. 2015Perturbed Iterate Analysis for Asynchronous Stochastic Optimizationcited by 67
  23. 2020Towards Flexible Device Participation in Federated Learningcited by 64
  24. 2015Staleness-aware Async-SGD for Distributed Deep Learningcited by 43
  25. 2017AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Trainingcited by 33
  26. 2021An Efficient Statistical-based Gradient Compression Technique for Distributed Training Systemscited by 32
  27. 2019Federated Learning for Ranking Browser History Suggestionscited by 23
  28. 2019Federated Learning with Autotuned Communication-Efficient Secure Aggregationcited by 23
  29. 2020FLeet: Online Federated Learning via Staleness Awareness and Performance Predictioncited by 21
  30. 2021FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systemscited by 16
  31. 2019I Know You'll Be Back: Interpretable New User Clustering and Churn Prediction on a Mobile Social Applicationcited by 10
  32. 2020Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Datacited by 9
  33. 2021Is Shapley Value fair? Improving Client Selection for Mavericks in Federated Learningcited by 7
  34. 2021On the Impact of Device and Behavioral Heterogeneity in Federated Learningcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 7; 2024: 8; 2025: 8; 2026: 2.

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  1. 2023FedZero: Leveraging Renewable Excess Energy in Federated Learningcited by 11
  2. 2024Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Studycited by 10
  3. 2023Green Federated Learningcited by 8
  4. 2022Federated Momentum Contrastive Clusteringcited by 6
  5. 2024Boosting Asynchronous Decentralized Learning with Model Fragmentationcited by 6
  6. 2024Flashback: Understanding and Mitigating Forgetting in Federated Learningcited by 4
  7. 2024LIFL: A Lightweight, Event-driven Serverless Platform for Federated Learningcited by 2
  8. 2024Learnable Sparse Customization in Heterogeneous Edge Computingcited by 2
  9. 2025Green Federated Learning via Carbon-Aware Client and Time Slot Schedulingcited by 2
  10. 2023Leveraging The Edge-to-Cloud Continuum for Scalable Machine Learning on Decentralized Datacited by 1
  11. 2023Federated Learning in IoT: a Survey from a Resource-Constrained Perspectivecited by 1
  12. 2023EcoLearn: Optimizing the Carbon Footprint of Federated Learningcited by 1
14 more
  1. 2023An Empirical Study of Efficiency and Privacy of Federated Learning Algorithmscited by 1
  2. 2024Context-Aware Orchestration of Energy-Efficient Gossip Learning Schemescited by 1
  3. 2023Knowledge Rumination for Client Utility Evaluation in Heterogeneous Federated Learningcited by 0
  4. 2024Harnessing Increased Client Participation with Cohort-Parallel Federated Learningcited by 0
  5. 2024Where is the Testbed for my Federated Learning Research?cited by 0
  6. 2025FLStore: Efficient Federated Learning Storage for non-training workloadscited by 0
  7. 2025Accelerating MoE Model Inference with Expert Shardingcited by 0
  8. 2025Benchmarking Mutual Information-based Loss Functions in Federated Learningcited by 0
  9. 2025FedOptima: Optimizing Resource Utilization in Federated Learningcited by 0
  10. 2025RIFLES: Resource-effIcient Federated LEarning via Schedulingcited by 0
  11. 2025FlexFed: Mitigating Catastrophic Forgetting in Heterogeneous Federated Learning in Pervasive Computing Environmentscited by 0
  12. 2025Optimizing Federated Learning for Scalable Power-demand Forecasting in Microgridscited by 0
  13. 2026FlashbackCL: Mitigating Temporal Forgetting in Federated Learningcited by 0
  14. 2026HASA: Subnet Allocation for Compute-Constrained Model-Heterogeneous Federated Learningcited by 0