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Heterogeneous Federated Learning via Personalized Generative Networks

arXiv 2308.13265 ↗First posted 25 Aug 2023cs.LGcited by 0

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Abstract (arXiv)

Federated Learning (FL) allows several clients to construct a common global machine-learning model without having to share their data. FL, however, faces the challenge of statistical heterogeneity between the client's data, which degrades performance and slows down the convergence toward the global model. In this paper, we provide theoretical proof that minimizing heterogeneity between clients facilitates the convergence of a global model for every single client. This becomes particularly important under empirical concept shifts among clients, rather than merely considering imbalanced classes, which have been studied until now. Therefore, we propose a method for knowledge transfer between clients where the server trains client-specific generators. Each generator generates samples for the corresponding client to remove the conflict with other clients' models. Experiments conducted on synthetic and real data, along with a theoretical study, support the effectiveness of our method in constructing a well-generalizable global model by reducing the conflict between local models.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 33 references that are in the map. First posted on arXiv 25 Aug 2023.

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18 of 33 references.

References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  3. 2015Domain-Adversarial Training of Neural Networkscited by 3,044
  4. 2018Federated Learning with Non-IID Datacited by 1,108
  5. 2023A Comprehensive Survey of Continual Learning: Theory, Method and Applicationcited by 872
  6. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  7. 2018LEAF: A Benchmark for Federated Settingscited by 792
  8. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 682
  9. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 383
  10. 2018Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Datacited by 243
  11. 2021FedMix: Approximation of Mixup under Mean Augmented Federated Learningcited by 108
  12. 2020FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoringcited by 61
6 more
  1. 2022Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillationcited by 45
  2. 2022A Survey on Heterogeneous Federated Learningcited by 42
  3. 2021Local-Global Knowledge Distillation in Heterogeneous Federated Learning with Non-IID Datacited by 31
  4. 2022FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentationcited by 24
  5. 2022Mitigating Data Heterogeneity in Federated Learning with Data Augmentationcited by 21
  6. 2022FedDKD: Federated Learning with Decentralized Knowledge Distillationcited by 9

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