# Re-thinking Federated Active Learning based on Inter-class Diversity

arXiv: https://arxiv.org/abs/2303.12317 · HTML: https://mlsanity.com/paper/2303.12317/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 53 references that are in the map.
- First posted on arXiv 22 Mar 2023.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Selective Classification for Deep Neural Networks](https://mlsanity.com/paper/1705.08500/) (2017)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Model-Contrastive Federated Learning](https://mlsanity.com/paper/2103.16257/) (2021)
- [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://mlsanity.com/paper/2110.14795/) (2021)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [Federated Learning with Matched Averaging](https://mlsanity.com/paper/2002.06440/) (2020)
- [Federated Learning Based on Dynamic Regularization](https://mlsanity.com/paper/2111.04263/) (2021)
- [Exploiting Shared Representations for Personalized Federated Learning](https://mlsanity.com/paper/2102.07078/) (2021)
- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [Variational Adversarial Active Learning](https://mlsanity.com/paper/1904.00370/) (2019)
- [FedBABU: Towards Enhanced Representation for Federated Image Classification](https://mlsanity.com/paper/2106.06042/) (2021)
- [FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction](https://mlsanity.com/paper/2203.11751/) (2022)
- [Adversarial Active Learning for Deep Networks: a Margin Based Approach](https://mlsanity.com/paper/1802.09841/) (2018)
- [Contextual Diversity for Active Learning](https://mlsanity.com/paper/2008.05723/) (2020)
- [Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning](https://mlsanity.com/paper/2006.12097/) (2020)
- [Deep Active Learning over the Long Tail](https://mlsanity.com/paper/1711.00941/) (2017)
- [Divergence-aware Federated Self-Supervised Learning](https://mlsanity.com/paper/2204.04385/) (2022)
- [Sequential Graph Convolutional Network for Active Learning](https://mlsanity.com/paper/2006.10219/) (2020)
- [Collaborative Unsupervised Visual Representation Learning from Decentralized Data](https://mlsanity.com/paper/2108.06492/) (2021)
- [Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applications](https://mlsanity.com/paper/1907.01132/) (2019)
- [Active Learning by Feature Mixing](https://mlsanity.com/paper/2203.07034/) (2022)
- [Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering](https://mlsanity.com/paper/2205.11506/) (2022)
- [Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning](https://mlsanity.com/paper/2202.00195/) (2022)
- [Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification](https://mlsanity.com/paper/2009.14448/) (2020)

## Built on by (most cited first, 14 of 14)

- [Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts](https://mlsanity.com/paper/2312.02567/) (2023)
- [Investigating the Quality of DermaMNIST and Fitzpatrick17k Dermatological Image Datasets](https://mlsanity.com/paper/2401.14497/) (2024)
- [ActiveDC: Distribution Calibration for Active Finetuning](https://mlsanity.com/paper/2311.07634/) (2023)
- [FAST: Federated Active Learning with Foundation Models for Communication-efficient Sampling and Training](https://mlsanity.com/paper/2504.03783/) (2025)
- [Active Learning via Vision-Language Model Adaptation with Open Data](https://mlsanity.com/paper/2506.01724/) (2025)
- [Exploring the Possibility of TypiClust for Low-Budget Federated Active Learning](https://mlsanity.com/paper/2505.19404/) (2025)
- [Federated Active Learning for Target Domain Generalisation](https://mlsanity.com/paper/2312.02247/) (2023)
- [Differentially Private Active Learning: Balancing Effective Data Selection and Privacy](https://mlsanity.com/paper/2410.00542/) (2024)
- [Multi-Label Bayesian Active Learning with Inter-Label Relationships](https://mlsanity.com/paper/2411.17941/) (2024)
- [Federated Learning with Workload Reduction through Partial Training of Client Models and Entropy-Based Data Selection](https://mlsanity.com/paper/2501.00170/) (2024)
- [Federated Active Learning Under Extreme Non-IID and Global Class Imbalance](https://mlsanity.com/paper/2603.10341/) (2026)
- [BESplit: Bias-Compensated Split Federated Learning with Evidential Aggregation](https://mlsanity.com/paper/2605.17508/) (2026)
- [Coordination on a Budget: Federated Active Learning with Few Labels](https://mlsanity.com/paper/2608.18634/) (2026)
- [FIDAL: Diversity-Aware Federated Active Learning Under Real-World Distribution Shifts](https://mlsanity.com/paper/2609.31637/) (2026)

## Abstract (arXiv)

Although federated learning has made awe-inspiring advances, most studies have assumed that the client's data are fully labeled. However, in a real-world scenario, every client may have a significant amount of unlabeled instances. Among the various approaches to utilizing unlabeled data, a federated active learning framework has emerged as a promising solution. In the decentralized setting, there are two types of available query selector models, namely 'global' and 'local-only' models, but little literature discusses their performance dominance and its causes. In this work, we first demonstrate that the superiority of two selector models depends on the global and local inter-class diversity. Furthermore, we observe that the global and local-only models are the keys to resolving the imbalance of each side. Based on our findings, we propose LoGo, a FAL sampling strategy robust to varying local heterogeneity levels and global imbalance ratio, that integrates both models by two steps of active selection scheme. LoGo consistently outperforms six active learning strategies in the total number of 38 experimental settings.