mlsanityData to 7 Oct 2026

Communication Efficiency in Federated Learning: Achievements and Challenges

arXiv 2107.10996 ↗First posted 23 Jul 2021cs.LGcited by 40

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Abstract (arXiv)

Federated Learning (FL) is known to perform Machine Learning tasks in a distributed manner. Over the years, this has become an emerging technology especially with various data protection and privacy policies being imposed FL allows performing machine learning tasks whilst adhering to these challenges. As with the emerging of any new technology, there are going to be challenges and benefits. A challenge that exists in FL is the communication costs, as FL takes place in a distributed environment where devices connected over the network have to constantly share their updates this can create a communication bottleneck. In this paper, we present a survey of the research that is performed to overcome the communication constraints in an FL setting.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). Builds on 53 of its 76 references that are in the map. First posted on arXiv 23 Jul 2021.

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53 of 76 references.

References · 53

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  5. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  6. 2018Federated Learning with Non-IID Datacited by 1,099
  7. 2017Federated Multi-Task Learningcited by 669
  8. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  9. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  10. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  11. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  12. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 349
38 more
  1. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  2. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 222
  3. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 208
  4. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 202
  5. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 196
  6. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
  7. 2020FetchSGD: Communication-Efficient Federated Learning with Sketchingcited by 178
  8. 2020Threats to Federated Learning: A Surveycited by 171
  9. 2013Communication Efficient Distributed Optimization using an Approximate Newton-type Methodcited by 131
  10. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 128
  11. 2019One-Shot Federated Learningcited by 124
  12. 2019BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learningcited by 115
  13. 2019Federated Learning for Emoji Prediction in a Mobile Keyboardcited by 115
  14. 2019Decentralized Deep Learning with Arbitrary Communication Compressioncited by 109
  15. 2020UVeQFed: Universal Vector Quantization for Federated Learningcited by 103
  16. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 102
  17. 2019Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregationcited by 99
  18. 2020Communication-Efficient Edge AI: Algorithms and Systemscited by 95
  19. 2019Reliable Federated Learning for Mobile Networkscited by 92
  20. 2014Deep learning with Elastic Averaging SGDcited by 90
  21. 2018Communication Compression for Decentralized Trainingcited by 87
  22. 2019Peer-to-peer Federated Learning on Graphscited by 85
  23. 2020Privacy-preserving Traffic Flow Prediction: A Federated Learning Approachcited by 82
  24. 2020FedDANE: A Federated Newton-Type Methodcited by 76
  25. 2016CoCoA: A General Framework for Communication-Efficient Distributed Optimizationcited by 69
  26. 2020Fast-Convergent Federated Learningcited by 61
  27. 2020Federated Learning With Quantized Global Model Updatescited by 54
  28. 2018Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communicationcited by 51
  29. 2019Federated Learning Of Out-Of-Vocabulary Wordscited by 51
  30. 2019HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learningcited by 51
  31. 2020Design and Analysis of Uplink and Downlink Communications for Federated Learningcited by 48
  32. 2016AIDE: Fast and Communication Efficient Distributed Optimizationcited by 42
  33. 2020From Federated Learning to Federated Neural Architecture Search: A Surveycited by 42
  34. 2019Central Server Free Federated Learning over Single-sided Trust Social Networkscited by 33
  35. 2018Efficient Decentralized Deep Learning by Dynamic Model Averagingcited by 30
  36. 2018Partitioned Variational Inference: A unified framework encompassing federated and continual learningcited by 29
  37. 2019Hyper-Sphere Quantization: Communication-Efficient SGD for Federated Learningcited by 18
  38. 2019Compressing Gradient Optimizers via Count-Sketchescited by 17

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Citing papers per year: 2022: 5; 2023: 8; 2024: 12; 2025: 8; 2026: 7.

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  1. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  2. 2023When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directionscited by 83
  3. 2022ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsitycited by 45
  4. 2023Personalized Federated Learning under Mixture of Distributionscited by 37
  5. 2022Faster federated optimization under second-order similaritycited by 18
  6. 2023Multimodal Federated Learning in Healthcare: a Reviewcited by 13
  7. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
  8. 2023Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classificationcited by 10
  9. 2023Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learningcited by 9
  10. 2024Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutionscited by 8
  11. 2024Promoting Data and Model Privacy in Federated Learning through Quantized LoRAcited by 8
  12. 2024Federated Learning with Quantum Computing and Fully Homomorphic Encryption: A Novel Computing Paradigm Shift in Privacy-Preserving MLcited by 8
28 more
  1. 2024Federated Learning with Label-Masking Distillationcited by 7
  2. 2024Communication-Efficient Federated Low-Rank Update Algorithm and its Connection to Implicit Regularizationcited by 6
  3. 2024SPD-CFL: Stepwise Parameter Dropout for Efficient Continual Federated Learningcited by 5
  4. 2025Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Modelscited by 5
  5. 2022OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsificationcited by 4
  6. 2024Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspectivecited by 4
  7. 2023Learning to Transmit with Provable Guarantees in Wireless Federated Learningcited by 3
  8. 2023A review of federated learning in renewable energy applications: Potential, challenges, and future directionscited by 3
  9. 2022Adaptive Differential Filters for Fast and Communication-Efficient Federated Learningcited by 2
  10. 2025Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Surveycited by 2
  11. 2025FedMPDD: Communication-Efficient Federated Learning with Privacy Preservation Attributes via Projected Directional Derivativecited by 2
  12. 2026FedMOA: Federated GRPO for Personalized Reasoning LLMs under Heterogeneous Rewardscited by 2
  13. 2024Feature-based Federated Transfer Learning: Communication Efficiency, Robustness and Privacycited by 1
  14. 2024SCALE: Self-regulated Clustered federAted LEarning in a Homogeneous Environmentcited by 1
  15. 2025Enhancing Model Privacy in Federated Learning with Random Masking and Quantizationcited by 1
  16. 2026Self-Play Enhancement via Advantage-Weighted Refinement in Online Federated LLM Fine-Tuning with Real-Time Feedbackcited by 1
  17. 2024FedShift: Robust Federated Learning Aggregation Scheme in Resource Constrained Environment via Weight Shiftingcited by 0
  18. 2024CELLM: An Efficient Communication in Large Language Models Training for Federated Learningcited by 0
  19. 2024A Robust Federated Learning Framework for Undependable Devices at Scalecited by 0
  20. 2025Encoded Spatial Attribute in Multi-Tier Federated Learningcited by 0
  21. 2025UltraFlwr -- An Efficient Federated Surgical Object Detection Frameworkcited by 0
  22. 2025Provable Reduction in Communication Rounds for Non-Smooth Convex Federated Learningcited by 0
  23. 2025Privacy Preserved Federated Learning with Attention-Based Aggregation for Biometric Recognitioncited by 0
  24. 2026Communication-efficient Federated Graph Classification via Generative Diffusion Modelingcited by 0
  25. 2026A Full Compression Pipeline for Green Federated Learning in Communication-Constrained Environmentscited by 0
  26. 2026COSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communicationcited by 0
  27. 2026Quantization in Federated Learning: Methods, Challenges and Future Directionscited by 0
  28. 2026MUC-FL: Block-Wise Marginal Utility Contribution for Communication-Efficient Federated Learningcited by 0