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Personalized Federated Learning for Statistical Heterogeneity

arXiv 2402.10254 ↗First posted 7 Feb 2024cs.LG cs.NIcited by 0

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Abstract (arXiv)

The popularity of federated learning (FL) is on the rise, along with growing concerns about data privacy in artificial intelligence applications. FL facilitates collaborative multi-party model learning while simultaneously ensuring the preservation of data confidentiality. Nevertheless, the problem of statistical heterogeneity caused by the presence of diverse client data distributions gives rise to certain challenges, such as inadequate personalization and slow convergence. In order to address the above issues, this paper offers a brief summary of the current research progress in the field of personalized federated learning (PFL). It outlines the PFL concept, examines related techniques, and highlights current endeavors. Furthermore, this paper also discusses potential further research and obstacles associated with PFL.

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References · 20

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2018Federated Learning with Non-IID Datacited by 1,099
  4. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  5. 2017Federated Multi-Task Learningcited by 669
  6. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  7. 2019Federated Learning with Personalization Layerscited by 533
  8. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  9. 2021Towards Personalized Federated Learningcited by 462
  10. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 454
  11. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
  12. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
8 more
  1. 2019SCAFFOLD: Stochastic Controlled Averaging for Federated Learningcited by 174
  2. 2020Privacy and Robustness in Federated Learning: Attacks and Defensescited by 163
  3. 2020Survey of Personalization Techniques for Federated Learningcited by 144
  4. 2019Federated Learning Of Out-Of-Vocabulary Wordscited by 51
  5. 2019Private Federated Learning with Domain Adaptationcited by 36
  6. 2018Federated Learning for Ultra-Reliable Low-Latency V2V Communicationscited by 30
  7. 2019An Investigation Into On-device Personalization of End-to-end Automatic Speech Recognition Modelscited by 25
  8. 2022A Joint Framework to Privacy-Preserving Edge Intelligence in Vehicular Networkscited by 2

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