mlsanityData to 14 Oct 2026

GRP-FED: Addressing Client Imbalance in Federated Learning via Global-Regularized Personalization

arXiv 2108.13858 ↗First posted 31 Aug 2021cs.LG cs.AIcited by 4

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Abstract (arXiv)

Since data is presented long-tailed in reality, it is challenging for Federated Learning (FL) to train across decentralized clients as practical applications. We present Global-Regularized Personalization (GRP-FED) to tackle the data imbalanced issue by considering a single global model and multiple local models for each client. With adaptive aggregation, the global model treats multiple clients fairly and mitigates the global long-tailed issue. Each local model is learned from the local data and aligns with its distribution for customization. To prevent the local model from just overfitting, GRP-FED applies an adversarial discriminator to regularize between the learned global-local features. Extensive results show that our GRP-FED improves under both global and local scenarios on real-world MIT-BIH and synthesis CIFAR-10 datasets, achieving comparable performance and addressing client imbalance.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 16 of its 23 references that are in the map. First posted on arXiv 31 Aug 2021.

Builds on

16 of 23 references.

References · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
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  11. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 269
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4 more
  1. 2020Federated Learning of a Mixture of Global and Local Modelscited by 210
  2. 2019Adaptive Gradient-Based Meta-Learning Methodscited by 183
  3. 2019Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computationscited by 181
  4. 2020Acceleration for Compressed Gradient Descent in Distributed and Federated Optimizationcited by 90

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Cited by year

Citing papers per year: 2023: 2; 2024: 2.

4 papers.

  1. 2023A Survey on Class Imbalance in Federated Learningcited by 13
  2. 2024FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Datacited by 10
  3. 2023Dynamic Heterogeneous Federated Learning with Multi-Level Prototypescited by 7
  4. 2024Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalizationcited by 2