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FedSub: Introducing Class-aware Subnetworks Fusion to Enhance Personalized Federated Learning

arXiv 2411.08699 ↗First posted 13 Nov 2024cs.LGcited by 0

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Abstract (arXiv)

Personalized Federated Learning aims at addressing the challenges of non-IID data in collaborative model training. However, existing methods struggle to balance personalization and generalization, often oversimplifying client similarities or relying too heavily on global models. In this paper, we propose FedSub, a novel approach that introduces class-aware model updates based on data prototypes and model subnetworks fusion to enhance personalization. Prototypes serve as compact representations of client data for each class, clustered on the server to capture label-specific similarities among the clients. Meanwhile, model subnetworks encapsulate the most relevant components to process each class and they are then fused on the server based on the identified clusters to generate fine-grained, class-specific, and highly personalized model updates for each client. Experimental results in three real-world scenarios with high data heterogeneity in human activity recognition and mobile health applications demonstrate the effectiveness of FedSub with respect to state-of-the-art methods to achieve fast convergence and high classification performance.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 59 references that are in the map. First posted on arXiv 13 Nov 2024.

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References · 22

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
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  5. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
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  11. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 349
  12. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 222
10 more
  1. 2021Federated Learning for Smart Healthcare: A Surveycited by 182
  2. 2021Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learningcited by 93
  3. 2019Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruningcited by 89
  4. 2019Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Recordscited by 70
  5. 2020FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoringcited by 61
  6. 2017Hierarchical Clustering: Objective Functions and Algorithmscited by 60
  7. 2016Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagationcited by 11
  8. 2021Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applicationscited by 11
  9. 2024Impact of network topology on the performance of Decentralized Federated Learningcited by 7
  10. 2020Dynamic Hard Pruning of Neural Networks at the Edge of the Internetcited by 4

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