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FedGraph: an Aggregation Method from Graph Perspective

arXiv 2210.02733 ↗First posted 6 Oct 2022cs.CVcited by 0

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Abstract (arXiv)

With the increasingly strengthened data privacy act and the difficult data centralization, Federated Learning (FL) has become an effective solution to collaboratively train the model while preserving each client's privacy. FedAvg is a standard aggregation algorithm that makes the proportion of dataset size of each client as aggregation weight. However, it can't deal with non-independent and identically distributed (non-i.i.d) data well because of its fixed aggregation weights and the neglect of data distribution. In this paper, we propose an aggregation strategy that can effectively deal with non-i.i.d dataset, namely FedGraph, which can adjust the aggregation weights adaptively according to the training condition of local models in whole training process. The FedGraph takes three factors into account from coarse to fine: the proportion of each local dataset size, the topology factor of model graphs, and the model weights. We calculate the gravitational force between local models by transforming the local models into topology graphs. The FedGraph can explore the internal correlation between local models better through the weighted combination of the proportion each local dataset, topology structure, and model weights. The proposed FedGraph has been applied to the MICCAI Federated Tumor Segmentation Challenge 2021 (FeTS) datasets, and the validation results show that our method surpasses the previous state-of-the-art by 2.76 mean Dice Similarity Score. The source code will be available at Github.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 28 references that are in the map. First posted on arXiv 6 Oct 2022.

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References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  4. 2018Federated Learning with Non-IID Datacited by 1,099
  5. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  6. 2020Federated Learning with Matched Averagingcited by 564
  7. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  8. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 284
  9. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  10. 2018Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentationcited by 148
  11. 2021Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learningcited by 78
  12. 2021The Federated Tumor Segmentation (FeTS) Challengecited by 49
6 more
  1. 2020Inverse Distance Aggregation for Federated Learning with Non-IID Datacited by 44
  2. 2019Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithmscited by 29
  3. 2021Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentationcited by 19
  4. 2021SplitAVG: A heterogeneity-aware federated deep learning method for medical imagingcited by 19
  5. 2022Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentationcited by 16
  6. 2021FedCostWAvg: A new averaging for better Federated Learningcited by 11

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