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SAPE-FL Enhances Personalized Federated Learning in Heterogeneous Settings.

Arun Kumar A V, Sunil Gupta, Dang Ngyuen, Bao Duong, Dat Phan Trong· September 3, 2026 View original

Key takeaways

  • SAPE-FL improves personalized federated learning in heterogeneous data environments.
  • It uses a dual-anchoring mechanism with global and similarity-weighted peer models.
  • Dynamic, client-specific regularization mitigates negative transfer.
  • The framework outperforms state-of-the-art methods, especially with high heterogeneity.

Who benefits

HealthcareFinanceIoTTelecommunicationsSmart Cities

Summary

This paper introduces SAPE-FL, a novel personalized federated learning framework that improves model performance in heterogeneous environments. It anchors each client's model to both a global model and a similarity-weighted peer-averaged model, dynamically adjusting regularization based on model and output similarity.

Federated Learning (FL) allows multiple decentralized clients to collaboratively train a shared model without centralizing their data, which is excellent for privacy. However, when data distributions vary significantly across clients (statistical heterogeneity), the global model often performs poorly, sometimes even worse than a model trained solely on local data. SAPE-FL (Similarity-Aware Personalized Federated Learning) addresses this by introducing a dual-anchoring mechanism. Each client's local model is regularized not only by the global model but also by an average of models from similar peers. This framework dynamically adjusts the regularization strength based on both model and output similarity, effectively filtering out dissimilar clients and preventing negative knowledge transfer. Theoretical analysis confirms its convergence, and empirical results demonstrate SAPE-FL's superior performance in highly heterogeneous and data-scarce client environments compared to existing state-of-the-art methods.

Why it matters

Organizations implementing federated learning can achieve more robust and personalized models, especially in scenarios where client data is diverse or limited, enhancing privacy-preserving AI applications.

How to implement this in your domain

  1. 1Evaluate SAPE-FL's potential for improving existing federated learning deployments with heterogeneous data.
  2. 2Develop mechanisms for calculating and leveraging client-specific model and output similarities.
  3. 3Integrate dynamic regularization based on similarity into federated training pipelines.
  4. 4Pilot SAPE-FL in privacy-sensitive applications where data remains decentralized.

Original post by Arun Kumar A V, Sunil Gupta, Dang Ngyuen, Bao Duong, Dat Phan Trong

"arXiv:2609.02241v1 Announce Type: new Abstract: Federated Learning (FL) allows decentralized clients to train models collaboratively while preserving data privacy. However, distribution mismatch across clients often leads to poor global generalization and degraded local client-le…"

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Originally posted by Arun Kumar A V, Sunil Gupta, Dang Ngyuen, Bao Duong, Dat Phan Trong on X · view source

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