mlsanityData to 13 Oct 2026

Flexible Vertical Federated Learning with Heterogeneous Parties

arXiv 2208.12672 ↗First posted 26 Aug 2022cs.DCcited by 20

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Abstract (arXiv)

We propose Flexible Vertical Federated Learning (Flex-VFL), a distributed machine algorithm that trains a smooth, non-convex function in a distributed system with vertically partitioned data. We consider a system with several parties that wish to collaboratively learn a global function. Each party holds a local dataset; the datasets have different features but share the same sample ID space. The parties are heterogeneous in nature: the parties' operating speeds, local model architectures, and optimizers may be different from one another and, further, they may change over time. To train a global model in such a system, Flex-VFL utilizes a form of parallel block coordinate descent, where parties train a partition of the global model via stochastic coordinate descent. We provide theoretical convergence analysis for Flex-VFL and show that the convergence rate is constrained by the party speeds and local optimizer parameters. We apply this analysis and extend our algorithm to adapt party learning rates in response to changing speeds and local optimizer parameters. Finally, we compare the convergence time of Flex-VFL against synchronous and asynchronous VFL algorithms, as well as illustrate the effectiveness of our adaptive extension.

Key facts. Cited by 20 papers in the map (data through 13 Oct 2026). First posted on arXiv 26 Aug 2022.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2022: 1; 2023: 9; 2024: 3; 2025: 6; 2026: 1.

20 papers.

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  1. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 108
  2. 2023FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Modelscited by 17
  3. 2024Advances in Robust Federated Learning: A Survey with Heterogeneity Considerationscited by 12
  4. 2023Vertical Federated Learning: Taxonomies, Threats, and Prospectscited by 8
  5. 2023VFLAIR: A Research Library and Benchmark for Vertical Federated Learningcited by 8
  6. 2023Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Partiescited by 7
  7. 2023Online Vertical Federated Learning for Cooperative Spectrum Sensingcited by 6
  8. 2024Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioningcited by 6
  9. 2024Communication-efficient Vertical Federated Learning via Compressed Error Feedbackcited by 3
  10. 2023Secure and Fast Asynchronous Vertical Federated Learning via Cascaded Hybrid Optimizationcited by 2
  11. 2023Robust Collaborative Inference with Vertically Split Data Over Dynamic Device Environmentscited by 2
  12. 2023Flexible Differentially Private Vertical Federated Learning with Adaptive Feature Embeddingscited by 1
8 more
  1. 2023Task-Oriented Edge Networks: Decentralized Learning Over Wireless Fronthaulcited by 1
  2. 2025Integrating Identity-Based Identification against Adaptive Adversaries in Federated Learningcited by 1
  3. 2025Communication-Efficient and Differentially Private Vertical Federated Learning with Zeroth-Order Optimizationcited by 0
  4. 2025Deep Latent Variable Model based Vertical Federated Learning with Flexible Alignment and Labeling Scenarioscited by 0
  5. 2025Multimodal Federated Learning: A Survey through the Lens of Different FL Paradigmscited by 0
  6. 2025Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Datacited by 0
  7. 2025PubSub-VFL: Towards Efficient Two-Party Split Learning in Heterogeneous Environments via Publisher/Subscriber Architecturecited by 0
  8. 2026Optimizing Server Placement for Vertical Federated Learning in Dynamic Edge/Fog Networkscited by 0