# Throughput-Optimal Topology Design for Cross-Silo Federated Learning

arXiv: https://arxiv.org/abs/2010.12229 · HTML: https://mlsanity.com/paper/2010.12229/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 30 of 39)

- [Federated Multi-Task Learning under a Mixture of Distributions](https://mlsanity.com/paper/2108.10252/) (2021)
- [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022)
- [FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings](https://mlsanity.com/paper/2210.04620/) (2022)
- [Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark](https://mlsanity.com/paper/2311.06750/) (2023)
- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [Deep Federated Learning for Autonomous Driving](https://mlsanity.com/paper/2110.05754/) (2021)
- [Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Data](https://mlsanity.com/paper/2204.04452/) (2022)
- [Membership Inference Attacks and Defenses in Federated Learning: A Survey](https://mlsanity.com/paper/2412.06157/) (2024)
- [Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matching](https://mlsanity.com/paper/2203.12285/) (2022)
- [Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey](https://mlsanity.com/paper/2302.02573/) (2023)
- [On the Convergence of Zeroth-Order Federated Tuning for Large Language Models](https://mlsanity.com/paper/2402.05926/) (2024)
- [Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspective](https://mlsanity.com/paper/2106.11814/) (2021)
- [D-Cliques: Compensating for Data Heterogeneity with Topology in Decentralized Federated Learning](https://mlsanity.com/paper/2104.07365/) (2021)
- [Distributed Pruning Towards Tiny Neural Networks in Federated Learning](https://mlsanity.com/paper/2212.01977/) (2022)
- [Who Leaked the Model? Tracking IP Infringers in Accountable Federated Learning](https://mlsanity.com/paper/2312.03205/) (2023)
- [Efficient and Reliable Overlay Networks for Decentralized Federated Learning](https://mlsanity.com/paper/2112.15486/) (2021)
- [Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence](https://mlsanity.com/paper/2305.11420/) (2023)
- [A review of Federated Learning in Intrusion Detection Systems for IoT](https://mlsanity.com/paper/2204.12443/) (2022)
- [Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression](https://mlsanity.com/paper/2211.14292/) (2022)
- [Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives](https://mlsanity.com/paper/2307.02140/) (2023)
- [Scalable Decentralized Learning with Teleportation](https://mlsanity.com/paper/2501.15259/) (2025)
- [Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice](https://mlsanity.com/paper/2510.12595/) (2025)
- [Federated Learning Survey: A Multi-Level Taxonomy of Aggregation Techniques, Experimental Insights, and Future Frontiers](https://mlsanity.com/paper/2511.22616/) (2025)
- [Cross-Silo Heterogeneous Model Federated Multitask Learning](https://mlsanity.com/paper/2202.08603/) (2022)
- [DeFL: Decentralized Weight Aggregation for Cross-silo Federated Learning](https://mlsanity.com/paper/2208.00848/) (2022)
- [Overlay-based Decentralized Federated Learning in Bandwidth-limited Networks](https://mlsanity.com/paper/2408.04705/) (2024)
- [FedEFM: Federated Endovascular Foundation Model with Unseen Data](https://mlsanity.com/paper/2501.16992/) (2025)
- [Reducing Training Time in Cross-Silo Federated Learning using Multigraph Topology](https://mlsanity.com/paper/2207.09657/) (2022)
- [Decentralized Hyper-Gradient Computation over Time-Varying Directed Networks](https://mlsanity.com/paper/2210.02129/) (2022)
- [FedST: Secure Federated Shapelet Transformation for Time Series Classification](https://mlsanity.com/paper/2302.10631/) (2023)

## Abstract (arXiv)

Federated learning usually employs a client-server architecture where an orchestrator iteratively aggregates model updates from remote clients and pushes them back a refined model. This approach may be inefficient in cross-silo settings, as close-by data silos with high-speed access links may exchange information faster than with the orchestrator, and the orchestrator may become a communication bottleneck. In this paper we define the problem of topology design for cross-silo federated learning using the theory of max-plus linear systems to compute the system throughput---number of communication rounds per time unit. We also propose practical algorithms that, under the knowledge of measurable network characteristics, find a topology with the largest throughput or with provable throughput guarantees. In realistic Internet networks with 10 Gbps access links for silos, our algorithms speed up training by a factor 9 and 1.5 in comparison to the master-slave architecture and to state-of-the-art MATCHA, respectively. Speedups are even larger with slower access links.