mlsanityData to 7 Oct 2026

Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative Learning

arXiv 2008.06233 ↗First posted 14 Aug 2020cs.LG stat.MLcited by 19

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Abstract (arXiv)

The privacy-preserving federated learning for vertically partitioned data has shown promising results as the solution of the emerging multi-party joint modeling application, in which the data holders (such as government branches, private finance and e-business companies) collaborate throughout the learning process rather than relying on a trusted third party to hold data. However, existing federated learning algorithms for vertically partitioned data are limited to synchronous computation. To improve the efficiency when the unbalanced computation/communication resources are common among the parties in the federated learning system, it is essential to develop asynchronous training algorithms for vertically partitioned data while keeping the data privacy. In this paper, we propose an asynchronous federated SGD (AFSGD-VP) algorithm and its SVRG and SAGA variants on the vertically partitioned data. Moreover, we provide the convergence analyses of AFSGD-VP and its SVRG and SAGA variants under the condition of strong convexity. We also discuss their model privacy, data privacy, computational complexities and communication costs. To the best of our knowledge, AFSGD-VP and its SVRG and SAGA variants are the first asynchronous federated learning algorithms for vertically partitioned data. Extensive experimental results on a variety of vertically partitioned datasets not only verify the theoretical results of AFSGD-VP and its SVRG and SAGA variants, but also show that our algorithms have much higher efficiency than the corresponding synchronous algorithms.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Aug 2020.

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Citing papers per year: 2021: 9; 2022: 8; 2023: 0; 2024: 0; 2025: 2.

19 papers.

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  1. 2022A Survey on Heterogeneous Federated Learningcited by 42
  2. 2021Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updatingcited by 37
  3. 2022Fair and efficient contribution valuation for vertical federated learningcited by 18
  4. 2021Architectural Patterns for the Design of Federated Learning Systemscited by 17
  5. 2021Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challengescited by 17
  6. 2021AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilizationcited by 16
  7. 2022Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updatescited by 15
  8. 2022Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clusteringcited by 14
  9. 2021FLRA: A Reference Architecture for Federated Learning Systemscited by 9
  10. 2022Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithmcited by 9
  11. 2022Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicinecited by 8
  12. 2021A Vertical Federated Learning Framework for Horizontally Partitioned Labelscited by 4
7 more
  1. 2021Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platformcited by 4
  2. 2022Latency Aware Semi-synchronous Client Selection and Model Aggregation for Wireless Federated Learningcited by 3
  3. 2021A Vertical Federated Learning Method For Multi-Institutional Credit Scoring: MICScited by 2
  4. 2025FedOne: Query-Efficient Federated Learning for Black-box Discrete Prompt Learningcited by 1
  5. 2021An Information-Theoretic Analysis of The Cost of Decentralization for Learning and Inference Under Privacy Constraintscited by 0
  6. 2022Decision Models for Selecting Federated Learning Architecture Patternscited by 0
  7. 2025Event-Driven Online Vertical Federated Learningcited by 0