mlsanityData to 14 Oct 2026

Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioning

arXiv 2404.10110 ↗First posted 15 Apr 2024cs.LG cs.DCcited by 6

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Abstract (arXiv)

E-health allows smart devices and medical institutions to collaboratively collect patients' data, which is trained by Artificial Intelligence (AI) technologies to help doctors make diagnosis. By allowing multiple devices to train models collaboratively, federated learning is a promising solution to address the communication and privacy issues in e-health. However, applying federated learning in e-health faces many challenges. First, medical data is both horizontally and vertically partitioned. Since single Horizontal Federated Learning (HFL) or Vertical Federated Learning (VFL) techniques cannot deal with both types of data partitioning, directly applying them may consume excessive communication cost due to transmitting a part of raw data when requiring high modeling accuracy. Second, a naive combination of HFL and VFL has limitations including low training efficiency, unsound convergence analysis, and lack of parameter tuning strategies. In this paper, we provide a thorough study on an effective integration of HFL and VFL, to achieve communication efficiency and overcome the above limitations when data is both horizontally and vertically partitioned. Specifically, we propose a hybrid federated learning framework with one intermediate result exchange and two aggregation phases. Based on this framework, we develop a Hybrid Stochastic Gradient Descent (HSGD) algorithm to train models. Then, we theoretically analyze the convergence upper bound of the proposed algorithm. Using the convergence results, we design adaptive strategies to adjust the training parameters and shrink the size of transmitted data. Experimental results validate that the proposed HSGD algorithm can achieve the desired accuracy while reducing communication cost, and they also verify the effectiveness of the adaptive strategies.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 25 of its 62 references that are in the map. First posted on arXiv 15 Apr 2024.

Builds on

25 of 62 references.

References · 25

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
  3. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 690
  4. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  5. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
  6. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  7. 2014Understanding Deep Image Representations by Inverting Themcited by 403
  8. 2017Multitask learning and benchmarking with clinical time series datacited by 290
  9. 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 222
  10. 2019Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computationscited by 181
  11. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 176
  12. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 108
13 more
  1. 2019Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregationcited by 100
  2. 2020Ternary Compression for Communication-Efficient Federated Learningcited by 57
  3. 2018Multi-objective Evolutionary Federated Learningcited by 42
  4. 2022Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Datacited by 36
  5. 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
  6. 2021FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Datacited by 31
  7. 2021Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computationcited by 29
  8. 2020Adaptive Federated Learning and Digital Twin for Industrial Internet of Thingscited by 27
  9. 2022Flexible Vertical Federated Learning with Heterogeneous Partiescited by 20
  10. 2021Fast Federated Learning by Balancing Communication Trade-Offscited by 18
  11. 2021PIVODL: Privacy-preserving vertical federated learning over distributed labelscited by 9
  12. 2021Multi-Tier Federated Learning for Vertically Partitioned Datacited by 7
  13. 2021Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioningcited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 1.

6 papers.

  1. 2024Federated and Transfer Learning for Cancer Detection Based on Image Analysiscited by 3
  2. 2025Unlearning Clients, Features and Samples in Vertical Federated Learningcited by 3
  3. 2024A Hybrid Federated Kernel Regularized Least Squares Algorithmcited by 0
  4. 2024A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional Datacited by 0
  5. 2025Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitionscited by 0
  6. 2026Handling Covariate Mismatch in Collaborative Linear Predictioncited by 0