mlsanityData to 14 Oct 2026

FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning

arXiv 2410.14075 ↗First posted 17 Oct 2024cs.LGcited by 1

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Abstract (arXiv)

Federated learning (FL) enables multiple clients with distributed data sources to collaboratively train a shared model without compromising data privacy. However, existing FL paradigms face challenges due to heterogeneity in client data distributions and system capabilities. Personalized federated learning (pFL) has been proposed to mitigate these problems, but often requires a shared model architecture and a central entity for parameter aggregation, resulting in scalability and communication issues. More recently, model-heterogeneous FL has gained attention due to its ability to support diverse client models, but existing methods are limited by their dependence on a centralized framework, synchronized training, and publicly available datasets. To address these limitations, we introduce Federated Peer-Adaptive Ensemble Learning (FedPAE), a fully decentralized pFL algorithm that supports model heterogeneity and asynchronous learning. Our approach utilizes a peer-to-peer model sharing mechanism and ensemble selection to achieve a more refined balance between local and global information. Experimental results show that FedPAE outperforms existing state-of-the-art pFL algorithms, effectively managing diverse client capabilities and demonstrating robustness against statistical heterogeneity.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 32 of its 51 references that are in the map. First posted on arXiv 17 Oct 2024.

Builds on

32 of 51 references.

References · 32

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20 more
  1. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
  2. 2020An Efficient Framework for Clustered Federated Learningcited by 511
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Learned Neighbor Trust for Collaborative Deployment in Model-Agnostic Decentralized Learningcited by 0