mlsanityData to 7 Oct 2026

Study of the performance and scalability of federated learning for medical imaging with intermittent clients

arXiv 2207.08581 ↗First posted 18 Jul 2022cs.LG cs.AI cs.CVcited by 9

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Abstract (arXiv)

Federated learning is a data decentralization privacy-preserving technique used to perform machine or deep learning in a secure way. In this paper we present theoretical aspects about federated learning, such as the presentation of an aggregation operator, different types of federated learning, and issues to be taken into account in relation to the distribution of data from the clients, together with the exhaustive analysis of a use case where the number of clients varies. Specifically, a use case of medical image analysis is proposed, using chest X-Ray images obtained from an open data repository. In addition to the advantages related to privacy, improvements in predictions (in terms of accuracy, loss and area under the curve) and reduction of execution times will be studied with respect to the classical case (the centralized approach). Different clients will be simulated from the training data, selected in an unbalanced manner. The results of considering three or ten clients are exposed and compared between them and against the centralized case. Two different problems related to intermittent clients are discussed, together with two approaches to be followed for each of them. Specifically, this type of problems may occur because in a real scenario some clients may leave the training, and others enter it, and on the other hand because of client technical or connectivity problems. Finally, improvements and future work in the field are proposed.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 22 of its 50 references that are in the map. First posted on arXiv 18 Jul 2022.

Builds on

22 of 50 references.

References · 22

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  4. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  5. 2020The Future of Digital Health with Federated Learningcited by 625
  6. 2020Federated Learning with Matched Averagingcited by 564
  7. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  8. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  9. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
  10. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  11. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  12. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
10 more
  1. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
  2. 2021Federated Learning for Smart Healthcare: A Surveycited by 182
  3. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
  4. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  5. 2020Optimal Client Sampling for Federated Learningcited by 123
  6. 2019Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networkscited by 79
  7. 2019Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applicationscited by 64
  8. 2020Bandit-based Communication-Efficient Client Selection Strategies for Federated Learningcited by 29
  9. 2020Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacycited by 22
  10. 2020Distributed Additive Encryption and Quantization for Privacy Preserving Federated Deep Learningcited by 12

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Cited by year

Citing papers per year: 2022: 1; 2023: 4; 2024: 3; 2025: 1.

9 papers.

  1. 2023Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Modelscited by 49
  2. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 34
  3. 2023Multimodal Federated Learning in Healthcare: a Reviewcited by 13
  4. 2024Personalized Federated Learning for improving radar based precipitation nowcasting on heterogeneous areascited by 4
  5. 2023Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applicationscited by 3
  6. 2024Federated and Transfer Learning for Cancer Detection Based on Image Analysiscited by 3
  7. 2025Enhancing the Convergence of Federated Learning Aggregation Strategies with Limited Datacited by 2
  8. 2023Comparison of machine learning models applied on anonymized data with different techniquescited by 1
  9. 2024PAFedFV: Personalized and Asynchronous Federated Learning for Finger Vein Recognitioncited by 1