pFedKT: Personalized federated learning with dual knowledge transfer

To be global or personalized: Generalized federated learning with cooperative adaptation for data heterogeneity

K Ding, X Feng, H Yu - Knowledge-Based Systems, 2024 - Elsevier
As federated learning (FL) continues to advance, research has branched into two major
directions: enhancing the individual global model and developing multiple personalized …
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[PDF] arxiv.org

pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated Learning

L Yi, H Yu, C Ren, H Zhang, G Wang, X Liu… - arXiv preprint arXiv …, 2024 - arxiv.org
Model-heterogeneous personalized federated learning (MHPFL) enables FL clients to train
structurally different personalized models on non-independent and identically distributed …
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[PDF] arxiv.org

Federated Model Heterogeneous Matryoshka Representation Learning

L Yi, H Yu, C Ren, G Wang, X Liu, X Li - arXiv preprint arXiv:2406.00488, 2024 - arxiv.org
Model heterogeneous federated learning (MHeteroFL) enables FL clients to collaboratively
train models with heterogeneous structures in a distributed fashion. However, existing …
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Personalized Federated Learning with Multiple Classifier Aggregation

S Zheng, Q Zhu, Q Lin, S Liu, KC Wong, J Li - Available at SSRN 4865278 - papers.ssrn.com
Personalized federated learning (PFL) has garnered attention due to its capability to
address statistical heterogeneity among clients. Typically, prevailing PFL methods …
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