mlsanityData to 7 Oct 2026

Continual Horizontal Federated Learning for Heterogeneous Data

arXiv 2203.02108 ↗First posted 4 Mar 2022cs.LGcited by 8

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Abstract (arXiv)

Federated learning is a promising machine learning technique that enables multiple clients to collaboratively build a model without revealing the raw data to each other. Among various types of federated learning methods, horizontal federated learning (HFL) is the best-studied category and handles homogeneous feature spaces. However, in the case of heterogeneous feature spaces, HFL uses only common features and leaves client-specific features unutilized. In this paper, we propose a HFL method using neural networks named continual horizontal federated learning (CHFL), a continual learning approach to improve the performance of HFL by taking advantage of unique features of each client. CHFL splits the network into two columns corresponding to common features and unique features, respectively. It jointly trains the first column by using common features through vanilla HFL and locally trains the second column by using unique features and leveraging the knowledge of the first one via lateral connections without interfering with the federated training of it. We conduct experiments on various real world datasets and show that CHFL greatly outperforms vanilla HFL that only uses common features and local learning that uses all features that each client has.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 27 of its 46 references that are in the map. First posted on arXiv 4 Mar 2022.

Builds on

27 of 46 references.

References · 27

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2016Progressive Neural Networkscited by 1,332
  4. 2018Federated Learning with Non-IID Datacited by 1,099
  5. 2017Federated Multi-Task Learningcited by 669
  6. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  7. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  8. 2020Adaptive Personalized Federated Learningcited by 353
  9. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  10. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
  11. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 174
  12. 2020Federated Continual Learning with Weighted Inter-client Transfercited by 158
15 more
  1. 2020Privacy Preserving Vertical Federated Learning for Tree-based Modelscited by 89
  2. 2019Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinatorcited by 51
  3. 2019Federated Forestcited by 49
  4. 2021A distillation-based approach integrating continual learning and federated learning for pervasive servicescited by 42
  5. 2021Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updatingcited by 37
  6. 2020Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Datacited by 35
  7. 2021PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNNcited by 35
  8. 2020Continual Local Training for Better Initialization of Federated Modelscited by 34
  9. 2021FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Datacited by 31
  10. 2020FederBoost: Private Federated Learning for GBDTcited by 27
  11. 2018Entity Resolution and Federated Learning get a Federated Resolutioncited by 23
  12. 2021Vertical Federated Learning without Revealing Intersection Membershipcited by 19
  13. 2021Federated Reconnaissance: Efficient, Distributed, Class-Incremental Learningcited by 14
  14. 2021Privacy-Preserving Self-Taught Federated Learning for Heterogeneous Datacited by 5
  15. 2021Multi-Party Dual Learningcited by 3

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Cited by year

Citing papers per year: 2022: 1; 2023: 3; 2024: 2; 2025: 2.

8 papers.

  1. 2023Federated Continual Learning via Knowledge Fusion: A Surveycited by 53
  2. 2025Federated Continual Learning: Concepts, Challenges, and Solutionscited by 13
  3. 2022Application of federated learning techniques for arrhythmia classification using 12-lead ECG signalscited by 9
  4. 2023Personalised Federated Learning On Heterogeneous Feature Spacescited by 5
  5. 2023A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniquescited by 4
  6. 2025GraphFedMIG: Tackling Class Imbalance in Federated Graph Learning via Mutual Information-Guided Generationcited by 1
  7. 2024Survey of Privacy Threats and Countermeasures in Federated Learningcited by 0
  8. 2024GeFL: Model-Agnostic Federated Learning with Generative Modelscited by 0