# Cross-Silo Federated Learning: Challenges and Opportunities

arXiv: https://arxiv.org/abs/2206.12949 · HTML: https://mlsanity.com/paper/2206.12949/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 26 Jun 2022.

## Builds on (most cited first, 9 of 9)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [Robust Aggregation for Federated Learning](https://mlsanity.com/paper/1912.13445/) (2019)
- [Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning](https://mlsanity.com/paper/2108.10241/) (2021)
- [Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation](https://mlsanity.com/paper/2010.00753/) (2020)
- [Vertical Federated Learning: Challenges, Methodologies and Experiments](https://mlsanity.com/paper/2202.04309/) (2022)
- [Motivating Workers in Federated Learning: A Stackelberg Game Perspective](https://mlsanity.com/paper/1908.03092/) (2019)

## Built on by (most cited first, 30 of 38)

- [Federated Generative Learning with Foundation Models](https://mlsanity.com/paper/2306.16064/) (2023)
- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions](https://mlsanity.com/paper/2411.12377/) (2024)
- [G2uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering](https://mlsanity.com/paper/2306.04984/) (2023)
- [Understanding Outer Optimizers in Local SGD: Learning Rates, Momentum, and Acceleration](https://mlsanity.com/paper/2509.10439/) (2025)
- [Federated Learning for ASR based on Wav2vec 2.0](https://mlsanity.com/paper/2302.10790/) (2023)
- [FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data](https://mlsanity.com/paper/2411.14717/) (2024)
- [FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission](https://mlsanity.com/paper/2403.00881/) (2024)
- [Quantifying the Impact of Label Noise on Federated Learning](https://mlsanity.com/paper/2211.07816/) (2022)
- [Federated Learning from Molecules to Processes: A Perspective](https://mlsanity.com/paper/2506.18525/) (2025)
- [Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice](https://mlsanity.com/paper/2510.12595/) (2025)
- [How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learning](https://mlsanity.com/paper/2401.13236/) (2024)
- [Distributionally Robust Clustered Federated Learning: A Case Study in Healthcare](https://mlsanity.com/paper/2410.07039/) (2024)
- [QuanCrypt-FL: Quantized Homomorphic Encryption with Pruning for Secure Federated Learning](https://mlsanity.com/paper/2411.05260/) (2024)
- [Technical and Legal Aspects of Federated Learning in Bioinformatics: Applications, Challenges and Opportunities](https://mlsanity.com/paper/2503.09649/) (2025)
- [Cross-Silo Federated Learning Across Divergent Domains with Iterative Parameter Alignment](https://mlsanity.com/paper/2311.04818/) (2023)
- [Federated learning in food research](https://mlsanity.com/paper/2406.06202/) (2024)
- [Privacy-preserving gradient-based fair federated learning](https://mlsanity.com/paper/2407.13881/) (2024)
- [PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning](https://mlsanity.com/paper/2502.08829/) (2025)
- [Exploring the Possibility of TypiClust for Low-Budget Federated Active Learning](https://mlsanity.com/paper/2505.19404/) (2025)
- [FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning](https://mlsanity.com/paper/2507.07258/) (2025)
- [BadFU: Backdoor Federated Learning through Adversarial Machine Unlearning](https://mlsanity.com/paper/2508.15541/) (2025)
- [Benchmarking Federated Learning in Edge Computing Environments: A Systematic Review and Performance Evaluation](https://mlsanity.com/paper/2603.08735/) (2026)
- [Scalable Cross-Facility Federated Learning for Scientific Foundation Models on Multiple Supercomputers](https://mlsanity.com/paper/2603.19544/) (2026)
- [Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysis](https://mlsanity.com/paper/2609.15681/) (2026)
- [Applications of Federated Learning in IoT for Hyper Personalisation](https://mlsanity.com/paper/2309.16854/) (2023)
- [A Blockchain Solution for Collaborative Machine Learning over IoT](https://mlsanity.com/paper/2311.14136/) (2023)
- [Exploring Federated Deep Learning for Standardising Naming Conventions in Radiotherapy Data](https://mlsanity.com/paper/2402.08999/) (2024)
- [A Biased Estimator for MinMax Sampling and Distributed Aggregation](https://mlsanity.com/paper/2404.17690/) (2024)
- [Data Distribution Shifts in (Industrial) Federated Learning as a Privacy Issue](https://mlsanity.com/paper/2409.13875/) (2024)

## 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.