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Murali Ponaganti

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#federated learning Open access Sep 2026

Fedadasparse: Communication-Efficient Personalized Federated Learning Via Similarity-Based Client Clustering And Adaptive Gradient Sparsification For Non-IID Edge Data

Federated learning (FL) enables collaborative model training across distributed clients without centralizing raw data, but its practical deployment on resource-constrained edge devices is hindered by two persistent challenges: statistical heterogeneity (non-independent and identically distributed, non-IID, client data)...

Murali Ponaganti · 0 citations
#federated learning Open access Sep 2026

Fedadasparse: Communication-Efficient Personalized Federated Learning Via Similarity-Based Client Clustering And Adaptive Gradient Sparsification For Non-IID Edge Data

Federated learning (FL) enables collaborative model training across distributed clients without centralizing raw data, but its practical deployment on resource-constrained edge devices is hindered by two persistent challenges: statistical heterogeneity (non-independent and identically distributed, non-IID, client data)...

Murali Ponaganti · 0 citations

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