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Event-Driven Online Vertical Federated Learning

arXiv 2506.14911 ↗First posted 17 Jun 2025cs.LG cs.DCcited by 0

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Abstract (arXiv)

Online learning is more adaptable to real-world scenarios in Vertical Federated Learning (VFL) compared to offline learning. However, integrating online learning into VFL presents challenges due to the unique nature of VFL, where clients possess non-intersecting feature sets for the same sample. In real-world scenarios, the clients may not receive data streaming for the disjoint features for the same entity synchronously. Instead, the data are typically generated by an event relevant to only a subset of clients. We are the first to identify these challenges in online VFL, which have been overlooked by previous research. To address these challenges, we proposed an event-driven online VFL framework. In this framework, only a subset of clients were activated during each event, while the remaining clients passively collaborated in the learning process. Furthermore, we incorporated dynamic local regret (DLR) into VFL to address the challenges posed by online learning problems with non-convex models within a non-stationary environment. We conducted a comprehensive regret analysis of our proposed framework, specifically examining the DLR under non-convex conditions with event-driven online VFL. Extensive experiments demonstrated that our proposed framework was more stable than the existing online VFL framework under non-stationary data conditions while also significantly reducing communication and computation costs.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 40 of its 65 references that are in the map. First posted on arXiv 17 Jun 2025.

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40 of 65 references.

References · 40

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2019Introduction to Online Convex Optimizationcited by 803
  4. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 571
  5. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  6. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 395
  7. 2018Online Learning: A Comprehensive Surveycited by 205
  8. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 176
  9. 2019Distributed Learning with Compressed Gradient Differencescited by 112
  10. 2020VAFL: a Method of Vertical Asynchronous Federated Learningcited by 87
  11. 2017Online Deep Learning: Learning Deep Neural Networks on the Flycited by 86
  12. 2020Vertically Federated Graph Neural Network for Privacy-Preserving Node Classificationcited by 61
28 more
  1. 2021Personalized Federated Learning through Local Memorizationcited by 57
  2. 2022Vertical Federated Learning: Challenges, Methodologies and Experimentscited by 55
  3. 2022Communication-Efficient Adaptive Federated Learningcited by 55
  4. 2019A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regressioncited by 38
  5. 2021Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updatingcited by 37
  6. 2022Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Datacited by 36
  7. 2019Online Non-Convex Learning: Following the Perturbed Leader is Optimalcited by 32
  8. 2020FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Trainingcited by 32
  9. 2017Efficient Regret Minimization in Non-Convex Gamescited by 28
  10. 2020Large-Scale Secure XGB for Vertical Federated Learningcited by 26
  11. 2014Differentially Private Distributed Optimizationcited by 24
  12. 2018Learning in Non-convex Games with an Optimization Oraclecited by 24
  13. 2022FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learningcited by 23
  14. 2020Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative Learningcited by 19
  15. 2019Dynamic Local Regret for Non-convex Online Forecastingcited by 17
  16. 2022A Hybrid Self-Supervised Learning Framework for Vertical Federated Learningcited by 17
  17. 2021AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilizationcited by 16
  18. 2022Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updatescited by 15
  19. 2022Tighter Regret Analysis and Optimization of Online Federated Learningcited by 14
  20. 2022Vertical Semi-Federated Learning for Efficient Online Advertisingcited by 12
  21. 2022Vertical Federated Edge Learning with Distributed Integrated Sensing and Communicationcited by 11
  22. 2020Online non-convex optimization with imperfect feedbackcited by 10
  23. 2022Online Federated Learning via Non-Stationary Detection and Adaptation amidst Concept Driftcited by 10
  24. 2022Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithmcited by 9
  25. 2023Online Vertical Federated Learning for Cooperative Spectrum Sensingcited by 6
  26. 2022Differentially Private Vertical Federated Learningcited by 4
  27. 2023Secure and Fast Asynchronous Vertical Federated Learning via Cascaded Hybrid Optimizationcited by 2
  28. 2024Vertical Federated Learning Hybrid Local Pre-trainingcited by 1

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