# Dynamic Clustering in Federated Learning

arXiv: https://arxiv.org/abs/2012.03788 · HTML: https://mlsanity.com/paper/2012.03788/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2020.

## Built on by (most cited first, 27 of 27)

- [Towards Federated Clustering: A Federated Fuzzy c-Means Algorithm (FFCM)](https://mlsanity.com/paper/2201.07316/) (2022)
- [DELTA: Diverse Client Sampling for Fasting Federated Learning](https://mlsanity.com/paper/2205.13925/) (2022)
- [Secure Federated Clustering](https://mlsanity.com/paper/2205.15564/) (2022)
- [Contrastive encoder pre-training-based clustered federated learning for heterogeneous data](https://mlsanity.com/paper/2311.16535/) (2023)
- [ModularFed: Leveraging Modularity in Federated Learning Frameworks](https://mlsanity.com/paper/2212.10427/) (2022)
- [Federated cINN Clustering for Accurate Clustered Federated Learning](https://mlsanity.com/paper/2309.01515/) (2023)
- [Open-Source AI-based SE Tools: Opportunities and Challenges of Collaborative Software Learning](https://mlsanity.com/paper/2404.06201/) (2024)
- [Robust Convergence in Federated Learning through Label-wise Clustering](https://mlsanity.com/paper/2112.14244/) (2021)
- [Survey of Federated Learning Models for Spatial-Temporal Mobility Applications](https://mlsanity.com/paper/2305.05257/) (2023)
- [Dynamically Weighted Federated k-Means](https://mlsanity.com/paper/2310.14858/) (2023)
- [How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learning](https://mlsanity.com/paper/2401.13236/) (2024)
- [Using Diffusion Models as Generative Replay in Continual Federated Learning -- What will Happen?](https://mlsanity.com/paper/2411.06618/) (2024)
- [Clustered FedStack: Intermediate Global Models with Bayesian Information Criterion](https://mlsanity.com/paper/2309.11044/) (2023)
- [Enhancing Federated Survival Analysis through Peer-Driven Client Reputation in Healthcare](https://mlsanity.com/paper/2505.16190/) (2025)
- [Beyond Aggregation: Guiding Clients in Heterogeneous Federated Learning](https://mlsanity.com/paper/2509.23049/) (2025)
- [A Joint Gradient and Loss Based Clustered Federated Learning Design](https://mlsanity.com/paper/2311.13665/) (2023)
- [Architectural Blueprint For Heterogeneity-Resilient Federated Learning](https://mlsanity.com/paper/2403.04546/) (2024)
- [CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering](https://mlsanity.com/paper/2407.12287/) (2024)
- [Comparative Evaluation of Clustered Federated Learning Methods](https://mlsanity.com/paper/2410.14212/) (2024)
- [Wasserstein-based Kernel Principal Component Analysis for Clustering Applications](https://mlsanity.com/paper/2503.14357/) (2025)
- [Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data](https://mlsanity.com/paper/2505.02540/) (2025)
- [RIFLES: Resource-effIcient Federated LEarning via Scheduling](https://mlsanity.com/paper/2505.13169/) (2025)
- [Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data](https://mlsanity.com/paper/2509.07198/) (2025)
- [DFCA: Decentralized Federated Clustering Algorithm](https://mlsanity.com/paper/2510.15300/) (2025)
- [Computation-aware Energy-harvesting Federated Learning with Pipelined Cyclic Scheduling](https://mlsanity.com/paper/2511.11949/) (2025)
- [Learning to Collaborate via Structures: Cluster-Guided Item Alignment for Federated Recommendation](https://mlsanity.com/paper/2602.21957/) (2026)
- [A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning](https://mlsanity.com/paper/2604.23386/) (2026)

## Abstract (arXiv)

In the resource management of wireless networks, Federated Learning has been used to predict handovers. However, non-independent and identically distributed data degrade the accuracy performance of such predictions. To overcome the problem, Federated Learning can leverage data clustering algorithms and build a machine learning model for each cluster. However, traditional data clustering algorithms, when applied to the handover prediction, exhibit three main limitations: the risk of data privacy breach, the fixed shape of clusters, and the non-adaptive number of clusters. To overcome these limitations, in this paper, we propose a three-phased data clustering algorithm, namely: generative adversarial network-based clustering, cluster calibration, and cluster division. We show that the generative adversarial network-based clustering preserves privacy. The cluster calibration deals with dynamic environments by modifying clusters. Moreover, the divisive clustering explores the different number of clusters by repeatedly selecting and dividing a cluster into multiple clusters. A baseline algorithm and our algorithm are tested on a time series forecasting task. We show that our algorithm improves the performance of forecasting models, including cellular network handover, by 43%.