mlsanityData to 7 Oct 2026

Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentation

arXiv 2301.12617 ↗First posted 30 Jan 2023cs.LG cs.AI cs.DCcited by 4

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Abstract (arXiv)

In federated learning (FL), the global model at the server requires an efficient mechanism for weight aggregation and a systematic strategy for collaboration selection to manage and optimize communication payload. We introduce a practical and cost-efficient method for regularized weight aggregation and propose a laborsaving technique to select collaborators per round. We illustrate the performance of our method, regularized similarity weight aggregation (RegSimAgg), on the Federated Tumor Segmentation (FeTS) 2022 challenge's federated training (weight aggregation) problem. Our scalable approach is principled, frugal, and suitable for heterogeneous non-IID collaborators. Using FeTS2021 evaluation criterion, our proposed algorithm RegSimAgg stands at 3rd position in the final rankings of FeTS2022 challenge in the weight aggregation task. Our solution is open sourced at: urlhttps://github.com/dskhanirfan/FeTS2022

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 15 of its 26 references that are in the map. First posted on arXiv 30 Jan 2023.

Builds on

15 of 26 references.

References · 15

  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. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2018Federated Learning with Non-IID Datacited by 1,099
  5. 2021Model-Contrastive Federated Learningcited by 762
  6. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 507
  7. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 284
  8. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  9. 2022Federated Learning Enables Big Data for Rare Cancer Boundary Detectioncited by 83
  10. 2021The Federated Tumor Segmentation (FeTS) Challengecited by 49
  11. 2022FedMix: Mixed Supervised Federated Learning for Medical Image Segmentationcited by 22
  12. 2021Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentationcited by 19
3 more
  1. 2022FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentationcited by 12
  2. 2022Federated Cross Learning for Medical Image Segmentationcited by 9
  3. 2022MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learningcited by 3

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Cited by year (2026 to 7 Oct)

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

4 papers.

  1. 2024Navigating Distribution Shifts in Medical Image Analysis: A Surveycited by 5
  2. 2024Dual-Criterion Model Aggregation in Federated Learning: Balancing Data Quantity and Qualitycited by 0
  3. 2025Privacy-Aware Federated nnU-Net for ECG Page Digitizationcited by 0
  4. 2026Similarity Weighted Aggregation with Global Differential Privacy for Federated Brain Lesion Segmentationcited by 0