mlsanityData to 7 Oct 2026

Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learning

arXiv 2602.20450 ↗First posted 24 Feb 2026cs.DC cs.LGcited by 0

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Abstract (arXiv)

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, providing strong justification for the robustness of Terraform.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 42 of its 72 references that are in the map. First posted on arXiv 24 Feb 2026.

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References · 42

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30 more
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