mlsanityData to 7 Oct 2026

Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioning

arXiv 2108.08930 ↗First posted 19 Aug 2021cs.LG cs.DCcited by 6

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Abstract (arXiv)

We consider federated learning in tiered communication networks. Our network model consists of a set of silos, each holding a vertical partition of the data. Each silo contains a hub and a set of clients, with the silo's vertical data shard partitioned horizontally across its clients. We propose Tiered Decentralized Coordinate Descent (TDCD), a communication-efficient decentralized training algorithm for such two-tiered networks. The clients in each silo perform multiple local gradient steps before sharing updates with their hub to reduce communication overhead. Each hub adjusts its coordinates by averaging its workers' updates, and then hubs exchange intermediate updates with one another. We present a theoretical analysis of our algorithm and show the dependence of the convergence rate on the number of vertical partitions and the number of local updates. We further validate our approach empirically via simulation-based experiments using a variety of datasets and objectives.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 25 of its 38 references that are in the map. First posted on arXiv 19 Aug 2021.

Builds on

25 of 38 references.

References · 25

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,140
  5. 2018Local SGD Converges Fast and Communicates Littlecited by 595
  6. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  7. 2019Federated Machine Learning: Concept and Applicationscited by 409
  8. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  9. 2017Multitask learning and benchmarking with clinical time series datacited by 287
  10. 2018Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learningcited by 277
  11. 2019Tighter Theory for Local SGD on Identical and Heterogeneous Datacited by 266
  12. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
13 more
  1. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
  2. 2019On the Convergence of Local Descent Methods in Federated Learningcited by 150
  3. 2018Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithmscited by 106
  4. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
  5. 2020Privacy Preserving Vertical Federated Learning for Tree-based Modelscited by 89
  6. 2020VAFL: a Method of Vertical Asynchronous Federated Learningcited by 87
  7. 2019MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Samplingcited by 67
  8. 2019Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinatorcited by 51
  9. 2020Multi-Participant Multi-Class Vertical Federated Learningcited by 47
  10. 2013Distributed Coordinate Descent Method for Learning with Big Datacited by 23
  11. 2020Multi-Level Local SGD for Heterogeneous Hierarchical Networkscited by 9
  12. 2021Multi-Tier Federated Learning for Vertically Partitioned Datacited by 7
  13. 2014A distributed block coordinate descent method for training l1 regularized linear classifierscited by 4

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Cited by year

Citing papers per year: 2023: 1; 2024: 4; 2025: 1.

6 papers.

  1. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
  2. 2024Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directionscited by 12
  3. 2024Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioningcited by 6
  4. 2024A Differentially Private Blockchain-Based Approach for Vertical Federated Learningcited by 3
  5. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  6. 2024Personalized Multi-tier Federated Learningcited by 0