mlsanityData to 14 Oct 2026

Federated Computing -- Survey on Building Blocks, Extensions and Systems

arXiv 2404.02779 ↗First posted 3 Apr 2024cs.LGcited by 0

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Abstract (arXiv)

In response to the increasing volume and sensitivity of data, traditional centralized computing models face challenges, such as data security breaches and regulatory hurdles. Federated Computing (FC) addresses these concerns by enabling collaborative processing without compromising individual data privacy. This is achieved through a decentralized network of devices, each retaining control over its data, while participating in collective computations. The motivation behind FC extends beyond technical considerations to encompass societal implications. As the need for responsible AI and ethical data practices intensifies, FC aligns with the principles of user empowerment and data sovereignty. FC comprises of Federated Learning (FL) and Federated Analytics (FA). FC systems became more complex over time and they currently lack a clear definition and taxonomy describing its moving pieces. Current surveys capture domain-specific FL use cases, describe individual components in an FC pipeline individually or decoupled from each other, or provide a quantitative overview of the number of published papers. This work surveys more than 150 papers to distill the underlying structure of FC systems with their basic building blocks, extensions, architecture, environment, and motivation. We capture FL and FA systems individually and point out unique difference between those two.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 58 of its 147 references that are in the map. First posted on arXiv 3 Apr 2024.

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58 of 147 references.

References · 58

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2019Towards Federated Learning at Scale: System Designcited by 832
  4. 2018LEAF: A Benchmark for Federated Settingscited by 792
  5. 2020The Future of Digital Health with Federated Learningcited by 633
  6. 2020Federated Learning with Matched Averagingcited by 564
  7. 2019Federated Learning with Personalization Layerscited by 541
  8. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  9. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 374
  10. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 356
  11. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
  12. 2021Federated Learning on Non-IID Data: A Surveycited by 346
38 more
  1. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 300
  2. 2018Scalable Private Learning with PATEcited by 269
  3. 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 222
  4. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 198
  5. 2020UVeQFed: Universal Vector Quantization for Federated Learningcited by 107
  6. 2018A generic framework for privacy preserving deep learningcited by 103
  7. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 103
  8. 2019DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compressioncited by 99
  9. 2020Privacy-preserving Traffic Flow Prediction: A Federated Learning Approachcited by 83
  10. 2020FedDANE: A Federated Newton-Type Methodcited by 76
  11. 2020FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tierscited by 64
  12. 2021OpenFL: An open-source framework for Federated Learningcited by 58
  13. 2020IBM Federated Learning: an Enterprise Framework White Paper V0.1cited by 57
  14. 2020Federated Learning With Quantized Global Model Updatescited by 55
  15. 2022FederatedScope: A Flexible Federated Learning Platform for Heterogeneitycited by 53
  16. 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
  17. 2019Energy-Efficient Radio Resource Allocation for Federated Edge Learningcited by 39
  18. 2020Advancements of federated learning towards privacy preservation: from federated learning to split learningcited by 34
  19. 2022Electrical Load Forecasting Using Edge Computing and Federated Learningcited by 34
  20. 2021A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparisoncited by 31
  21. 2020Energy-Efficient Federated Edge Learning with Joint Communication and Computation Designcited by 30
  22. 2021FEDZIP: A Compression Framework for Communication-Efficient Federated Learningcited by 27
  23. 2022FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulationscited by 27
  24. 2022APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learningcited by 20
  25. 2020An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategiescited by 18
  26. 2019Automatic Discovery of Privacy-Utility Pareto Frontscited by 16
  27. 2021Scalable federated machine learning with FEDncited by 16
  28. 2019Substra: a framework for privacy-preserving, traceable and collaborative Machine Learningcited by 14
  29. 2020FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated Learningcited by 14
  30. 2020GFL: A Decentralized Federated Learning Framework Based On Blockchaincited by 12
  31. 2021A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Thingscited by 11
  32. 2021FedNILM: Applying Federated Learning to NILM Applications at the Edgecited by 11
  33. 2022Towards Energy-Aware Federated Learning on Battery-Powered Clientscited by 11
  34. 2021Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregationcited by 10
  35. 2022Federated Learning based Energy Demand Prediction with Clustered Aggregationcited by 9
  36. 2020Edge-assisted Democratized Learning Towards Federated Analyticscited by 5
  37. 2021Efficient Hyperparameter Optimization for Differentially Private Deep Learningcited by 5
  38. 2021Fed-NILM: A Federated Learning-based Non-Intrusive Load Monitoring Method for Privacy-Protectioncited by 4

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