mlsanityData to 14 Oct 2026

Personalized Federated Learning with Adaptive Feature Aggregation and Knowledge Transfer

arXiv 2410.15073 ↗First posted 19 Oct 2024cs.LG cs.AI cs.CRcited by 2

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Abstract (arXiv)

Federated Learning(FL) is popular as a privacy-preserving machine learning paradigm for generating a single model on decentralized data. However, statistical heterogeneity poses a significant challenge for FL. As a subfield of FL, personalized FL (pFL) has attracted attention for its ability to achieve personalized models that perform well on non-independent and identically distributed (Non-IID) data. However, existing pFL methods are limited in terms of leveraging the global model's knowledge to enhance generalization while achieving personalization on local data. To address this, we proposed a new method personalized Federated learning with Adaptive Feature Aggregation and Knowledge Transfer (FedAFK), to train better feature extractors while balancing generalization and personalization for participating clients, which improves the performance of personalized models on Non-IID data. We conduct extensive experiments on three datasets in two widely-used heterogeneous settings and show the superior performance of our proposed method over thirteen state-of-the-art baselines.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 40 of its 63 references that are in the map. First posted on arXiv 19 Oct 2024.

Builds on

40 of 63 references.

References · 40

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28 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Evaluating Federated Learning for At-Risk Student Prediction: A Comparative Analysis of Model Complexity and Data Balancingcited by 0
  2. 2026Similarity-Aware Personalized Federated Learning in Heterogeneous Environmentscited by 0