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Inverse Distance Aggregation for Federated Learning with Non-IID Data

arXiv 2008.07665 ↗First posted 17 Aug 2020cs.LG cs.CV stat.MLcited by 44

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Abstract (arXiv)

Federated learning (FL) has been a promising approach in the field of medical imaging in recent years. A critical problem in FL, specifically in medical scenarios is to have a more accurate shared model which is robust to noisy and out-of distribution clients. In this work, we tackle the problem of statistical heterogeneity in data for FL which is highly plausible in medical data where for example the data comes from different sites with different scanner settings. We propose IDA (Inverse Distance Aggregation), a novel adaptive weighting approach for clients based on meta-information which handles unbalanced and non-iid data. We extensively analyze and evaluate our method against the well-known FL approach, Federated Averaging as a baseline.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Aug 2020.

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Citing papers per year: 2021: 12; 2022: 12; 2023: 5; 2024: 7; 2025: 6; 2026: 2.

44 papers.

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  1. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
  2. 2021HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Imagescited by 62
  3. 2021Emerging Trends in Federated Learning: From Model Fusion to Federated X Learningcited by 35
  4. 2021Personalized Federated Learning with Adaptive Batchnorm for Healthcarecited by 26
  5. 2021Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learningcited by 25
  6. 2022Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoTcited by 25
  7. 2022DYNAFED: Tackling Client Data Heterogeneity with Global Dynamicscited by 22
  8. 2021Federated Learning with Taskonomy for Non-IID Datacited by 21
  9. 2022IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentationcited by 21
  10. 2021Memory-aware curriculum federated learning for breast cancer classificationcited by 20
  11. 2021Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryptioncited by 18
  12. 2022HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaversecited by 14
32 more
  1. 2024Federated Distillation: A Surveycited by 14
  2. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
  3. 2024Exploring Vacant Classes in Label-Skewed Federated Learningcited by 10
  4. 2024DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptationcited by 9
  5. 2021DQRE-SCnet: A novel hybrid approach for selecting users in Federated Learning with Deep-Q-Reinforcement Learning based on Spectral Clusteringcited by 7
  6. 2022Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Trainingcited by 7
  7. 2021FedHealth 2: Weighted Federated Transfer Learning via Batch Normalization for Personalized Healthcarecited by 6
  8. 2021MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentationcited by 6
  9. 2022Federated Learning with Server Learning: Enhancing Performance for Non-IID Datacited by 6
  10. 2024Navigating Distribution Shifts in Medical Image Analysis: A Surveycited by 5
  11. 2025Learning Critically: Selective Self Distillation in Federated Learning on Non-IID Datacited by 4
  12. 2022Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimizationcited by 3
  13. 2023Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Updatescited by 3
  14. 2021Gradual Federated Learning with Simulated Annealingcited by 2
  15. 2022Adaptive Personlization in Federated Learning for Highly Non-i.i.d. Datacited by 2
  16. 2025Redefining Clustered Federated Learning for System Identification: The Path of ClusterCraftcited by 2
  17. 2021OCTOPUS: Overcoming Performance andPrivatization Bottlenecks in Distributed Learningcited by 1
  18. 2022Shape-Aware Masking for Inpainting in Medical Imagingcited by 1
  19. 2023Federated Learning with Classifier Shift for Class Imbalancecited by 1
  20. 2024FCNCP: A Coupled Nonnegative CANDECOMP/PARAFAC Decomposition Based on Federated Learningcited by 1
  21. 2025The Impact Analysis of Delays in Asynchronous Federated Learning with Data Heterogeneity for Edge Intelligencecited by 1
  22. 2025FedLoRA-Optimizer: Federated LoRA Fine-Tuning with Global and Local Optimization in Heterogeneous Data Scenarioscited by 1
  23. 2022FedGraph: an Aggregation Method from Graph Perspectivecited by 0
  24. 2022GTFLAT: Game Theory Based Add-On For Empowering Federated Learning Aggregation Techniquescited by 0
  25. 2023When to Trust Aggregated Gradients: Addressing Negative Client Sampling in Federated Learningcited by 0
  26. 2023AI Models Close to your Chest: Robust Federated Learning Strategies for Multi-site CTcited by 0
  27. 2024Federated Impression for Learning with Distributed Heterogeneous Datacited by 0
  28. 2024Conformable Convolution for Topologically Aware Learning of Complex Anatomical Structurescited by 0
  29. 2025Invariant Federated Learning for Edge Intelligence: Mitigating Heterogeneity and Asynchrony via Exit Strategy and Invariant Penaltycited by 0
  30. 2025FedCLAM: Client Adaptive Momentum with Foreground Intensity Matching for Federated Medical Image Segmentationcited by 0
  31. 2026FedImp: Enhancing Federated Learning Convergence with Impurity-Based Weightingcited by 0
  32. 2026FedDRAW: Federated Dual Reputation Annealing Weighting for Heterogeneous Multi-Institutional Chest Radiograph Classificationcited by 0