mlsanityData to 14 Oct 2026

Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data

arXiv 2206.08330 ↗First posted 16 Jun 2022cs.LGcited by 36

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Abstract (arXiv)

We propose Compressed Vertical Federated Learning (C-VFL) for communication-efficient training on vertically partitioned data. In C-VFL, a server and multiple parties collaboratively train a model on their respective features utilizing several local iterations and sharing compressed intermediate results periodically. Our work provides the first theoretical analysis of the effect message compression has on distributed training over vertically partitioned data. We prove convergence of non-convex objectives at a rate of O(frac1√T) when the compression error is bounded over the course of training. We provide specific requirements for convergence with common compression techniques, such as quantization and top-k sparsification. Finally, we experimentally show compression can reduce communication by over 90% without a significant decrease in accuracy over VFL without compression.

Key facts. Cited by 36 papers in the map (data through 14 Oct 2026). Builds on 33 of its 60 references that are in the map. First posted on arXiv 16 Jun 2022.

Builds on

33 of 60 references.

References · 33

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,150
  5. 2019Towards Federated Learning at Scale: System Designcited by 832
  6. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
  7. 2018signSGD: Compressed Optimisation for Non-Convex Problemscited by 571
  8. 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 478
  9. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  10. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  11. 2014Understanding Deep Image Representations by Inverting Themcited by 403
  12. 2018Sparsified SGD with Memorycited by 376
21 more
  1. 2019Error Feedback Fixes SignSGD and other Gradient Compression Schemescited by 310
  2. 2018Distributed learning of deep neural network over multiple agentscited by 279
  3. 2017TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learningcited by 256
  4. 2018Don't Use Large Mini-Batches, Use Local SGDcited by 202
  5. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  6. 2016Practical Secure Aggregation for Federated Learning on User-Held Datacited by 178
  7. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 176
  8. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 176
  9. 2021Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysiscited by 131
  10. 2020UVeQFed: Universal Vector Quantization for Federated Learningcited by 107
  11. 2018SGD and Hogwild! Convergence Without the Bounded Gradients Assumptioncited by 95
  12. 2020VAFL: a Method of Vertical Asynchronous Federated Learningcited by 87
  13. 2020Multi-Participant Multi-Class Vertical Federated Learningcited by 47
  14. 2020SplitNN-driven Vertical Partitioningcited by 42
  15. 2021PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNNcited by 35
  16. 2019A Communication Efficient Collaborative Learning Framework for Distributed Featurescited by 21
  17. 2020Hybrid Federated Learning: Algorithms and Implementationcited by 14
  18. 2015SparkNet: Training Deep Networks in Sparkcited by 13
  19. 2021Multi-Tier Federated Learning for Vertically Partitioned Datacited by 7
  20. 2021Towards Secure and Practical Machine Learning via Secret Sharing and Random Permutationcited by 5
  21. 2016Secure Multi-Party Computation Based Privacy Preserving Extreme Learning Machine Algorithm Over Vertically Distributed Datacited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 11; 2024: 10; 2025: 7; 2026: 5.

36 papers.

Sort:

  1. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 108
  2. 2023Reducing Communication for Split Learning by Randomized Top-k Sparsificationcited by 22
  3. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  4. 2023LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learningcited by 14
  5. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  6. 2022Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMMcited by 10
  7. 2023VFLAIR: A Research Library and Benchmark for Vertical Federated Learningcited by 8
  8. 2024Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Datacited by 8
  9. 2023VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarkscited by 6
  10. 2023Online Vertical Federated Learning for Cooperative Spectrum Sensingcited by 6
  11. 2024Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioningcited by 6
  12. 2023Federated Deep Multi-View Clustering with Global Self-Supervisioncited by 5
24 more
  1. 2025Integrated Sensing, Communication, and Computation for Over-the-Air Federated Edge Learningcited by 5
  2. 2025Vertical Federated Learning in Practice: The Good, the Bad, and the Uglycited by 4
  3. 2023A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learningcited by 3
  4. 2024Communication-efficient Vertical Federated Learning via Compressed Error Feedbackcited by 3
  5. 2024A Differentially Private Blockchain-Based Approach for Vertical Federated Learningcited by 3
  6. 2023Secure and Fast Asynchronous Vertical Federated Learning via Cascaded Hybrid Optimizationcited by 2
  7. 2023Defending Label Inference Attacks in Split Learning under Regression Settingcited by 2
  8. 2023Robust Collaborative Inference with Vertically Split Data Over Dynamic Device Environmentscited by 2
  9. 2024Mask-Encoded Sparsification: Mitigating Biased Gradients in Communication-Efficient Split Learningcited by 2
  10. 2024Towards Active Participant Centric Vertical Federated Learning: Some Representations May Be All You Needcited by 2
  11. 2025FlexP-SFT: A Flexible Aggregation-Free Framework for On-Device Personalized Split Federated Fine-Tuning of LLMscited by 2
  12. 2024Hybrid FedGraph: An efficient hybrid federated learning algorithm using graph convolutional neural networkcited by 1
  13. 2024Vertical Federated Learning Hybrid Local Pre-trainingcited by 1
  14. 2026AutoEncoder-Compressed Parallel Split Learning for Pre-trained Model Fine-Tuningcited by 1
  15. 2023EASTER: Embedding Aggregation-based Heterogeneous Models Training in Vertical Federated Learningcited by 0
  16. 2024Computation and Communication Efficient Lightweighting Vertical Federated Learning for Smart Building IoTcited by 0
  17. 2025Multimodal Federated Learning: A Survey through the Lens of Different FL Paradigmscited by 0
  18. 2025Event-Driven Online Vertical Federated Learningcited by 0
  19. 2025X-VFL: A New Vertical Federated Learning Framework with Cross Completion and Decision Subspace Alignmentcited by 0
  20. 2025PubSub-VFL: Towards Efficient Two-Party Split Learning in Heterogeneous Environments via Publisher/Subscriber Architecturecited by 0
  21. 2026On the Convergence of Stochastic Gradient Descent with Perturbed Forward-Backward Passescited by 0
  22. 2026Learning Unknown Interdependencies for Decentralized Root Cause Analysis in Nonlinear Dynamical Systemscited by 0
  23. 2026Optimizing Server Placement for Vertical Federated Learning in Dynamic Edge/Fog Networkscited by 0
  24. 2026CausShield: Sample Reconstruction-Resilient Vertical FL via Causal Representation Learningcited by 0

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