Personalized Federated Learning via Learning Dynamic Graphs
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Abstract (arXiv)
Personalized Federated Learning (PFL) aims to train a personalized model for each client that is tailored to its local data distribution, learning fails to perform well on individual clients due to variations in their local data distributions. Most existing PFL methods focus on personalizing the aggregated global model for each client, neglecting the fundamental aspect of federated learning: the regulation of how client models are aggregated. Additionally, almost all of them overlook the graph structure formed by clients in federated learning. In this paper, we propose a novel method, Personalized Federated Learning with Graph Attention Network (pFedGAT), which captures the latent graph structure between clients and dynamically determines the importance of other clients for each client, enabling fine-grained control over the aggregation process. We evaluate pFedGAT across multiple data distribution scenarios, comparing it with twelve state of the art methods on three datasets: Fashion MNIST, CIFAR-10, and CIFAR-100, and find that it consistently performs well.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 31 of its 41 references that are in the map. First posted on arXiv 7 Mar 2025.
Builds on
31 of 41 references.
References · 31
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2017Graph Attention Networkscited by 8,854
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
- 2018Federated Optimization in Heterogeneous Networkscited by 2,971
- 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
- 2021Model-Contrastive Federated Learningcited by 762
- 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
- 2017Federated Multi-Task Learningcited by 669
- 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
- 2020Personalized Federated Learning with Moreau Envelopescited by 612
- 2020Federated Learning with Matched Averagingcited by 564
19 more
- 2019Federated Learning with Personalization Layerscited by 533
- 2020An Efficient Framework for Clustered Federated Learningcited by 505
- 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
- 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 454
- 2020Adaptive Personalized Federated Learningcited by 353
- 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 322
- 2020Personalized Federated Learning: A Meta-Learning Approachcited by 309
- 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 282
- 2021Personalized Federated Learning using Hypernetworkscited by 210
- 2020FedVision: An Online Visual Object Detection Platform Powered by Federated Learningcited by 121
- 2021FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Taskscited by 84
- 2019Variational Federated Multi-Task Learningcited by 69
- 2020FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measurecited by 45
- 2021FedCV: A Federated Learning Framework for Diverse Computer Vision Taskscited by 32
- 2022Personalized Federated Learning With Graphcited by 29
- 2022FedAdapter: Efficient Federated Learning for Modern NLPcited by 24
- 2024Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learningcited by 9
- 2024FedAH: Aggregated Head for Personalized Federated Learningcited by 2
- 2025FedAGHN: Personalized Federated Learning with Attentive Graph HyperNetworkscited by 1
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.