![[2602.20450] Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learning](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.20450] Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learning
Summary
The paper presents Terraform, a novel client selection methodology for federated learning that addresses client heterogeneity, achieving up to 47% higher accuracy over previous methods.
Why It Matters
As federated learning becomes increasingly important for privacy-preserving machine learning, understanding how to effectively select clients is crucial. Terraform's approach enhances model accuracy and efficiency, making it relevant for researchers and practitioners in the field.
Key Takeaways
- Terraform improves client selection in federated learning by addressing heterogeneity.
- The methodology uses gradient updates and a deterministic selection algorithm.
- Achieves up to 47% higher accuracy compared to prior client selection methods.
- Comprehensive ablation studies validate the robustness of the approach.
- Enhances efficiency in training time, making it practical for real-world applications.
Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2602.20450 (cs) [Submitted on 24 Feb 2026] Title:Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learning Authors:Nihal Balivada, Shrey Gupta, Shashank Shreedhar Bhatt, Suyash Gupta View a PDF of the paper titled Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learning, by Nihal Balivada and 3 other authors View PDF HTML (experimental) Abstract:Federated Learning (FL) enables a distributed client-server architecture where multiple clients collaboratively train a global Machine Learning (ML) model without sharing sensitive local data. However, FL often results in lower accuracy than traditional ML algorithms due to statistical heterogeneity across clients. Prior works attempt to address this by using model updates, such as loss and bias, from client models to select participants that can improve the global model's accuracy. However, these updates neither accurately represent a client's heterogeneity nor are their selection methods deterministic. We mitigate these limitations by introducing Terraform, a novel client selection methodology that uses gradient updates and a deterministic selection algorithm to select heterogeneous clients for retraining. This bi-pronged approach allows Terraform to achieve up to 47 percent higher accuracy over prior works. We further demonstrate its efficiency through comprehensive ablation studies and training time analyses, p...
