mlsanityData to 14 Oct 2026

FS-Real: Towards Real-World Cross-Device Federated Learning

arXiv 2303.13363 ↗First posted 23 Mar 2023cs.LGcited by 22

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Abstract (arXiv)

Federated Learning (FL) aims to train high-quality models in collaboration with distributed clients while not uploading their local data, which attracts increasing attention in both academia and industry. However, there is still a considerable gap between the flourishing FL research and real-world scenarios, mainly caused by the characteristics of heterogeneous devices and its scales. Most existing works conduct evaluations with homogeneous devices, which are mismatched with the diversity and variability of heterogeneous devices in real-world scenarios. Moreover, it is challenging to conduct research and development at scale with heterogeneous devices due to limited resources and complex software stacks. These two key factors are important yet underexplored in FL research as they directly impact the FL training dynamics and final performance, making the effectiveness and usability of FL algorithms unclear. To bridge the gap, in this paper, we propose an efficient and scalable prototyping system for real-world cross-device FL, FS-Real. It supports heterogeneous device runtime, contains parallelism and robustness enhanced FL server, and provides implementations and extensibility for advanced FL utility features such as personalization, communication compression and asynchronous aggregation. To demonstrate the usability and efficiency of FS-Real, we conduct extensive experiments with various device distributions, quantify and analyze the effect of the heterogeneous device and various scales, and further provide insights and open discussions about real-world FL scenarios. Our system is released to help to pave the way for further real-world FL research and broad applications involving diverse devices and scales.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). Builds on 42 of its 67 references that are in the map. First posted on arXiv 23 Mar 2023.

Builds on

42 of 67 references.

References · 42

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  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
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  12. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
30 more
  1. 2021Towards Personalized Federated Learningcited by 464
  2. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  3. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  4. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 374
  5. 2021Federated Learning on Non-IID Data: A Surveycited by 346
  6. 2021A Field Guide to Federated Optimizationcited by 315
  7. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 300
  8. 2019Asynchronous Federated Optimizationcited by 267
  9. 2021Pruning and Quantization for Deep Neural Network Acceleration: A Surveycited by 263
  10. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 252
  11. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 192
  12. 2021Federated Learning with Buffered Asynchronous Aggregationcited by 190
  13. 2021FedBABU: Towards Enhanced Representation for Federated Image Classificationcited by 182
  14. 2020Federated Learning with Compression: Unified Analysis and Sharp Guaranteescited by 160
  15. 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 136
  16. 2021Papaya: Practical, Private, and Scalable Federated Learningcited by 90
  17. 2021Federated Evaluation and Tuning for On-Device Personalization: System Design & Applicationscited by 85
  18. 2020A Blockchain-based Decentralized Federated Learning Framework with Committee Consensuscited by 84
  19. 2022NVIDIA FLARE: Federated Learning from Simulation to Real-Worldcited by 80
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  21. 2020Ternary Compression for Communication-Efficient Federated Learningcited by 57
  22. 2020IBM Federated Learning: an Enterprise Framework White Paper V0.1cited by 57
  23. 2022Communication-Efficient Adaptive Federated Learningcited by 55
  24. 2022FederatedScope: A Flexible Federated Learning Platform for Heterogeneitycited by 53
  25. 2022FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clientscited by 46
  26. 2022pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learningcited by 44
  27. 2022FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learningcited by 43
  28. 2021A Comprehensive Survey of Incentive Mechanism for Federated Learningcited by 38
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 6; 2024: 9; 2025: 6; 2026: 1.

22 papers.

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  1. 2023FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learningcited by 149
  2. 2024Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resourcescited by 93
  3. 2023Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytescited by 55
  4. 2023Efficient Personalized Federated Learning via Sparse Model-Adaptationcited by 38
  5. 2024On the Convergence of Zeroth-Order Federated Tuning for Large Language Modelscited by 19
  6. 2024Decentralized Directed Collaboration for Personalized Federated Learningcited by 9
  7. 2024COALA: A Practical and Vision-Centric Federated Learning Platformcited by 7
  8. 2023Asymmetrically Decentralized Federated Learningcited by 5
  9. 2024Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutionscited by 5
  10. 2025Adaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning of Language Modelscited by 4
  11. 2023FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systemscited by 3
  12. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
10 more
  1. 2025Efficient Data Valuation Approximation in Federated Learning: A Sampling-based Approachcited by 2
  2. 2024Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guaranteescited by 1
  3. 2024Defending Against Diverse Attacks in Federated Learning Through Consensus-Based Bi-Level Optimizationcited by 1
  4. 2025FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learningcited by 1
  5. 2023Applications of Federated Learning in IoT for Hyper Personalisationcited by 0
  6. 2024Where is the Testbed for my Federated Learning Research?cited by 0
  7. 2024Predicting Quality of Video Gaming Experience Using Global-Scale Telemetry Data and Federated Learningcited by 0
  8. 2025FedRP: A Communication-Efficient Approach for Differentially Private Federated Learning Using Random Projectioncited by 0
  9. 2025Lightweight and Robust Federated Data Valuationcited by 0
  10. 2026Sharpness-Aware Minimization for Generalized Embedding Learning in Federated Recommendationcited by 0