mlsanityData to 14 Oct 2026

Personalizing or Not: Dynamically Personalized Federated Learning with Incentives

arXiv 2208.06192 ↗First posted 12 Aug 2022cs.LG cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Personalized federated learning (FL) facilitates collaborations between multiple clients to learn personalized models without sharing private data. The mechanism mitigates the statistical heterogeneity commonly encountered in the system, i.e., non-IID data over different clients. Existing personalized algorithms generally assume all clients volunteer for personalization. However, potential participants might still be reluctant to personalize models since they might not work well. In this case, clients choose to use the global model instead. To avoid making unrealistic assumptions, we introduce the personalization rate, measured as the fraction of clients willing to train personalized models, into federated settings and propose DyPFL. This dynamically personalized FL technique incentivizes clients to participate in personalizing local models while allowing the adoption of the global model when it performs better. We show that the algorithmic pipeline in DyPFL guarantees good convergence performance, allowing it to outperform alternative personalized methods in a broad range of conditions, including variation in heterogeneity, number of clients, local epochs, and batch sizes.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 32 references that are in the map. First posted on arXiv 12 Aug 2022.

Builds on

19 of 32 references.

References · 19

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  4. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  5. 2014Deep Learning Face Attributes in the Wildcited by 3,693
  6. 2019Advances and Open Problems in Federated Learningcited by 3,046
  7. 2018LEAF: A Benchmark for Federated Settingscited by 792
  8. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
  9. 2017Federated Multi-Task Learningcited by 672
  10. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
  11. 2020Personalized Federated Learning with Moreau Envelopescited by 618
  12. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
7 more
  1. 2021Towards Personalized Federated Learningcited by 464
  2. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  3. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 283
  4. 2021Personalized Federated Learning using Hypernetworkscited by 212
  5. 2021Evaluating Gradient Inversion Attacks and Defenses in Federated Learningcited by 196
  6. 2019On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithmscited by 132
  7. 2020Collaborative Fairness in Federated Learningcited by 95

Built on by

Not cited yet.