mlsanityData to 7 Oct 2026

FedPDC:Federated Learning for Public Dataset Correction

arXiv 2302.12503 ↗First posted 24 Feb 2023cs.LG cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

As people pay more and more attention to privacy protection, Federated Learning (FL), as a promising distributed machine learning paradigm, is receiving more and more attention. However, due to the biased distribution of data on devices in real life, federated learning has lower classification accuracy than traditional machine learning in Non-IID scenarios. Although there are many optimization algorithms, the local model aggregation in the parameter server is still relatively traditional. In this paper, a new algorithm FedPDC is proposed to optimize the aggregation mode of local models and the loss function of local training by using the shared data sets in some industries. In many benchmark experiments, FedPDC can effectively improve the accuracy of the global model in the case of extremely unbalanced data distribution, while ensuring the privacy of the client data. At the same time, the accuracy improvement of FedPDC does not bring additional communication costs.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 29 references that are in the map. First posted on arXiv 24 Feb 2023.

Builds on

24 of 29 references.

References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  3. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  4. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  5. 2019Advances and Open Problems in Federated Learningcited by 3,021
  6. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  7. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  8. 2021Model-Contrastive Federated Learningcited by 762
  9. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  10. 2020Federated Learning with Matched Averagingcited by 564
  11. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 537
  12. 2019Federated Learning with Personalization Layerscited by 533
12 more
  1. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 454
  2. 2019Federated Machine Learning: Concept and Applicationscited by 409
  3. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  4. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 349
  5. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 322
  6. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  7. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  8. 2019SCAFFOLD: Stochastic Controlled Averaging for Federated Learningcited by 174
  9. 2020Federated Mutual Learningcited by 70
  10. 2021FedMe: Federated Learning via Model Exchangecited by 10
  11. 2022FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy Judgmentcited by 3
  12. 2022Knowledge Distillation via Weighted Ensemble of Teaching Assistantscited by 1

Built on by

Not cited yet.