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CCVA-FL: Cross-Client Variations Adaptive Federated Learning for Medical Imaging

arXiv 2407.11652 ↗First posted 16 Jul 2024cs.CV cs.AI cs.LGcited by 0

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Abstract (arXiv)

Federated Learning (FL) offers a privacy-preserving approach to train models on decentralized data. Its potential in healthcare is significant, but challenges arise due to cross-client variations in medical image data, exacerbated by limited annotations. This paper introduces Cross-Client Variations Adaptive Federated Learning (CCVA-FL) to address these issues. CCVA-FL aims to minimize cross-client variations by transforming images into a common feature space. It involves expert annotation of a subset of images from each client, followed by the selection of a client with the least data complexity as the target. Synthetic medical images are then generated using Scalable Diffusion Models with Transformers (DiT) based on the target client's annotated images. These synthetic images, capturing diversity and representing the original data, are shared with other clients. Each client then translates its local images into the target image space using image-to-image translation. The translated images are subsequently used in a federated learning setting to develop a server model. Our results demonstrate that CCVA-FL outperforms Vanilla Federated Averaging by effectively addressing data distribution differences across clients without compromising privacy.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 32 references that are in the map. First posted on arXiv 16 Jul 2024.

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References · 9

  1. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  4. 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 471
  5. 2019Federated Adversarial Domain Adaptationcited by 140
  6. 2020Federated Transfer Learning for EEG Signal Classificationcited by 36
  7. 2023Phoenix: A Federated Generative Diffusion Modelcited by 21
  8. 2022Federated Stain Normalization for Computational Pathologycited by 11
  9. 2023Self-Supervised Federated Learning for Fast MR Imagingcited by 5

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  3. 2026FedDP-PALD: A Privacy-Preserving Federated Latent Diffusion Framework with Prototype Aggregation for Medical Data Synthesis
  4. 2023Federated Learning with Diffusion Models for Privacy-Sensitive Vision Tasks
  5. 2025A New One-Shot Federated Learning Framework for Medical Imaging Classification with Feature-Guided Rectified Flow and Knowledge Distillation
  6. 2024Federated Data Model
  7. 2025FedAgentBench: Towards Automating Real-world Federated Medical Image Analysis with Server-Client LLM Agents
  8. 2025Federated CLIP for Resource-Efficient Heterogeneous Medical Image Classification