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Throughput-Optimal Topology Design for Cross-Silo Federated Learning

arXiv 2010.12229 ↗First posted 23 Oct 2020cs.LG cs.DC cs.NIcited by 37

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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.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Oct 2020.

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Citing papers per year: 2021: 5; 2022: 11; 2023: 7; 2024: 4; 2025: 9; 2026: 1.

37 papers.

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  1. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 189
  2. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137
  3. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 121
  4. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
  5. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  6. 2021Deep Federated Learning for Autonomous Drivingcited by 54
  7. 2022Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Datacited by 31
  8. 2022Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matchingcited by 24
  9. 2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Surveycited by 23
  10. 2024On the Convergence of Zeroth-Order Federated Tuning for Large Language Modelscited by 18
  11. 2021Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspectivecited by 14
  12. 2021D-Cliques: Compensating for Data Heterogeneity with Topology in Decentralized Federated Learningcited by 12
25 more
  1. 2022Distributed Pruning Towards Tiny Neural Networks in Federated Learningcited by 11
  2. 2021Efficient and Reliable Overlay Networks for Decentralized Federated Learningcited by 9
  3. 2023Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergencecited by 8
  4. 2022A review of Federated Learning in Intrusion Detection Systems for IoTcited by 4
  5. 2022Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compressioncited by 3
  6. 2023Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectivescited by 3
  7. 2025Scalable Decentralized Learning with Teleportationcited by 3
  8. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  9. 2025Federated Learning Survey: A Multi-Level Taxonomy of Aggregation Techniques, Experimental Insights, and Future Frontierscited by 3
  10. 2022Cross-Silo Heterogeneous Model Federated Multitask Learningcited by 2
  11. 2022DeFL: Decentralized Weight Aggregation for Cross-silo Federated Learningcited by 2
  12. 2024Overlay-based Decentralized Federated Learning in Bandwidth-limited Networkscited by 2
  13. 2025FedEFM: Federated Endovascular Foundation Model with Unseen Datacited by 2
  14. 2022Reducing Training Time in Cross-Silo Federated Learning using Multigraph Topologycited by 1
  15. 2022Decentralized Hyper-Gradient Computation over Time-Varying Directed Networkscited by 1
  16. 2023FedST: Secure Federated Shapelet Transformation for Time Series Classificationcited by 1
  17. 2023Cross-Silo Federated Learning Across Divergent Domains with Iterative Parameter Alignmentcited by 1
  18. 2024Energy-efficient Decentralized Learning via Graph Sparsificationcited by 1
  19. 2025DBA-DFL: Towards Distributed Backdoor Attacks with Network Detection in Decentralized Federated Learningcited by 1
  20. 2024Photon: Federated LLM Pre-Trainingcited by 0
  21. 2025Communication Optimization for Decentralized Learning atop Bandwidth-limited Edge Networkscited by 0
  22. 2025Lightweight Temporal Transformer Decomposition for Federated Autonomous Drivingcited by 0
  23. 2025DFCA: Decentralized Federated Clustering Algorithmcited by 0
  24. 2025Delta Sum Learning: an approach for fast and global convergence in Gossip Learningcited by 0
  25. 2026A Comparative Study of Federated Learning Aggregation Strategies under Homogeneous and Heterogeneous Data Distributionscited by 0