# Inverse Distance Aggregation for Federated Learning with Non-IID Data

arXiv: https://arxiv.org/abs/2008.07665 · HTML: https://mlsanity.com/paper/2008.07665/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2020.

## Built on by (most cited first, 30 of 44)

- [Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark](https://mlsanity.com/paper/2311.06750/) (2023)
- [HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images](https://mlsanity.com/paper/2112.10775/) (2021)
- [Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning](https://mlsanity.com/paper/2102.12920/) (2021)
- [Personalized Federated Learning with Adaptive Batchnorm for Healthcare](https://mlsanity.com/paper/2112.00734/) (2021)
- [Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning](https://mlsanity.com/paper/2110.00394/) (2021)
- [Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT](https://mlsanity.com/paper/2202.01512/) (2022)
- [IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation](https://mlsanity.com/paper/2204.08467/) (2022)
- [DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics](https://mlsanity.com/paper/2211.10878/) (2022)
- [Federated Learning with Taskonomy for Non-IID Data](https://mlsanity.com/paper/2103.15947/) (2021)
- [Memory-aware curriculum federated learning for breast cancer classification](https://mlsanity.com/paper/2107.02504/) (2021)
- [Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption](https://mlsanity.com/paper/2108.03437/) (2021)
- [Federated Distillation: A Survey](https://mlsanity.com/paper/2404.08564/) (2024)
- [HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaverse](https://mlsanity.com/paper/2211.03300/) (2022)
- [Federated Learning and Meta Learning: Approaches, Applications, and Directions](https://mlsanity.com/paper/2210.13111/) (2022)
- [Exploring Vacant Classes in Label-Skewed Federated Learning](https://mlsanity.com/paper/2401.02329/) (2024)
- [DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation](https://mlsanity.com/paper/2405.06368/) (2024)
- [DQRE-SCnet: A novel hybrid approach for selecting users in Federated Learning with Deep-Q-Reinforcement Learning based on Spectral Clustering](https://mlsanity.com/paper/2111.04105/) (2021)
- [Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training](https://mlsanity.com/paper/2201.12976/) (2022)
- [FedHealth 2: Weighted Federated Transfer Learning via Batch Normalization for Personalized Healthcare](https://mlsanity.com/paper/2106.01009/) (2021)
- [MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentation](https://mlsanity.com/paper/2109.09734/) (2021)
- [Federated Learning with Server Learning: Enhancing Performance for Non-IID Data](https://mlsanity.com/paper/2210.02614/) (2022)
- [Navigating Distribution Shifts in Medical Image Analysis: A Survey](https://mlsanity.com/paper/2411.05824/) (2024)
- [Learning Critically: Selective Self Distillation in Federated Learning on Non-IID Data](https://mlsanity.com/paper/2504.14694/) (2025)
- [Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimization](https://mlsanity.com/paper/2211.05554/) (2022)
- [Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Updates](https://mlsanity.com/paper/2306.06559/) (2023)
- [OCTOPUS: Overcoming Performance andPrivatization Bottlenecks in Distributed Learning](https://mlsanity.com/paper/2105.00602/) (2021)
- [Gradual Federated Learning with Simulated Annealing](https://mlsanity.com/paper/2110.05178/) (2021)
- [Adaptive Personlization in Federated Learning for Highly Non-i.i.d. Data](https://mlsanity.com/paper/2207.03448/) (2022)
- [Redefining Clustered Federated Learning for System Identification: The Path of ClusterCraft](https://mlsanity.com/paper/2505.16857/) (2025)
- [Shape-Aware Masking for Inpainting in Medical Imaging](https://mlsanity.com/paper/2207.05787/) (2022)

## 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.