mlsanityData to 7 Oct 2026

Cross-Silo Federated Learning: Challenges and Opportunities

arXiv 2206.12949 ↗First posted 26 Jun 2022cs.LG cs.AI cs.GTcited by 38

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Abstract (arXiv)

Federated learning (FL) is an emerging technology that enables the training of machine learning models from multiple clients while keeping the data distributed and private. Based on the participating clients and the model training scale, federated learning can be classified into two types: cross-device FL where clients are typically mobile devices and the client number can reach up to a scale of millions; cross-silo FL where clients are organizations or companies and the client number is usually small (e.g., within a hundred). While existing studies mainly focus on cross-device FL, this paper aims to provide an overview of the cross-silo FL. More specifically, we first discuss applications of cross-silo FL and outline its major challenges. We then provide a systematic overview of the existing approaches to the challenges in cross-silo FL by focusing on their connections and differences to cross-device FL. Finally, we discuss future directions and open issues that merit research efforts from the community.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). Builds on 9 of its 15 references that are in the map. First posted on arXiv 26 Jun 2022.

Builds on

9 of 15 references.

References · 9

  1. 2019Advances and Open Problems in Federated Learningcited by 3,021
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  3. 2019Data Shapley: Equitable Valuation of Data for Machine Learningcited by 617
  4. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  5. 2019Robust Aggregation for Federated Learningcited by 314
  6. 2021Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learningcited by 116
  7. 2020Model-sharing Games: Analyzing Federated Learning Under Voluntary Participationcited by 60
  8. 2022Vertical Federated Learning: Challenges, Methodologies and Experimentscited by 55
  9. 2019Motivating Workers in Federated Learning: A Stackelberg Game Perspectivecited by 45

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 7; 2024: 11; 2025: 14; 2026: 5.

38 papers.

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  1. 2023Federated Generative Learning with Foundation Modelscited by 23
  2. 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
  3. 2024Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directionscited by 12
  4. 2023G2uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clusteringcited by 9
  5. 2023Federated Learning for ASR based on Wav2vec 2.0cited by 7
  6. 2024FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Datacited by 7
  7. 2025Understanding Outer Optimizers in Local SGD: Learning Rates, Momentum, and Accelerationcited by 7
  8. 2024FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmissioncited by 4
  9. 2022Quantifying the Impact of Label Noise on Federated Learningcited by 3
  10. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  11. 2024How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learningcited by 2
  12. 2024Distributionally Robust Clustered Federated Learning: A Case Study in Healthcarecited by 2
26 more
  1. 2024QuanCrypt-FL: Quantized Homomorphic Encryption with Pruning for Secure Federated Learningcited by 2
  2. 2025Technical and Legal Aspects of Federated Learning in Bioinformatics: Applications, Challenges and Opportunitiescited by 2
  3. 2025Federated Learning from Molecules to Processes: A Perspectivecited by 2
  4. 2023Cross-Silo Federated Learning Across Divergent Domains with Iterative Parameter Alignmentcited by 1
  5. 2024Federated learning in food researchcited by 1
  6. 2024Privacy-preserving gradient-based fair federated learningcited by 1
  7. 2025PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learningcited by 1
  8. 2025Exploring the Possibility of TypiClust for Low-Budget Federated Active Learningcited by 1
  9. 2025BadFU: Backdoor Federated Learning through Adversarial Machine Unlearningcited by 1
  10. 2026Benchmarking Federated Learning in Edge Computing Environments: A Systematic Review and Performance Evaluationcited by 1
  11. 2026Scalable Cross-Facility Federated Learning for Scientific Foundation Models on Multiple Supercomputerscited by 1
  12. 2026Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysiscited by 1
  13. 2023Applications of Federated Learning in IoT for Hyper Personalisationcited by 0
  14. 2023A Blockchain Solution for Collaborative Machine Learning over IoTcited by 0
  15. 2024Exploring Federated Deep Learning for Standardising Naming Conventions in Radiotherapy Datacited by 0
  16. 2024A Biased Estimator for MinMax Sampling and Distributed Aggregationcited by 0
  17. 2024Data Distribution Shifts in (Industrial) Federated Learning as a Privacy Issuecited by 0
  18. 2025Privacy Preserving and Robust Aggregation for Cross-Silo Federated Learning in Non-IID Settingscited by 0
  19. 2025The More is not the Merrier: Investigating the Effect of Client Size on Federated Learningcited by 0
  20. 2025Black-Box Privacy Attacks on Shared Representations in Multitask Learningcited by 0
  21. 2025FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learningcited by 0
  22. 2025FedRP: A Communication-Efficient Approach for Differentially Private Federated Learning Using Random Projectioncited by 0
  23. 2025Federated Learning Framework for Scalable AI in Heterogeneous HPC and Cloud Environmentscited by 0
  24. 2025Federated Multi-Task Clusteringcited by 0
  25. 2026Resource-Adaptive Federated Text Generation with Differential Privacycited by 0
  26. 2026XFED: Non-Collusive Model Poisoning Attack Against Byzantine-Robust Federated Classifierscited by 0