mlsanityData to 14 Oct 2026

Flow: Per-Instance Personalized Federated Learning Through Dynamic Routing

arXiv 2211.15281 ↗First posted 28 Nov 2022cs.LGcited by 6

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Abstract (arXiv)

Personalization in Federated Learning (FL) aims to modify a collaboratively trained global model according to each client. Current approaches to personalization in FL are at a coarse granularity, i.e. all the input instances of a client use the same personalized model. This ignores the fact that some instances are more accurately handled by the global model due to better generalizability. To address this challenge, this work proposes Flow, a fine-grained stateless personalized FL approach. Flow creates dynamic personalized models by learning a routing mechanism that determines whether an input instance prefers the local parameters or its global counterpart. Thus, Flow introduces per-instance routing in addition to leveraging per-client personalization to improve accuracies at each client. Further, Flow is stateless which makes it unnecessary for a client to retain its personalized state across FL rounds. This makes Flow practical for large-scale FL settings and friendly to newly joined clients. Evaluations on Stackoverflow, Reddit, and EMNIST datasets demonstrate the superiority in prediction accuracy of Flow over state-of-the-art non-personalized and only per-client personalized approaches to FL.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 34 of its 48 references that are in the map. First posted on arXiv 28 Nov 2022.

Builds on

34 of 48 references.

References · 34

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
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  5. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  6. 2020Adaptive Federated Optimizationcited by 1,046
  7. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  8. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
  9. 2020Personalized Federated Learning with Moreau Envelopescited by 618
  10. 2017BranchyNet: Fast Inference via Early Exiting from Deep Neural Networkscited by 596
  11. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
  12. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
22 more
  1. 2021Towards Personalized Federated Learningcited by 464
  2. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  3. 2020Adaptive Personalized Federated Learningcited by 358
  4. 2017SkipNet: Learning Dynamic Routing in Convolutional Networkscited by 332
  5. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 325
  6. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 325
  7. 2019Searching for A Robust Neural Architecture in Four GPU Hourscited by 260
  8. 2021Personalized Federated Learning using Hypernetworkscited by 212
  9. 2021FedBABU: Towards Enhanced Representation for Federated Image Classificationcited by 182
  10. 2017Adaptive Neural Networks for Efficient Inferencecited by 163
  11. 2020Minibatch vs Local SGD for Heterogeneous Distributed Learningcited by 147
  12. 2022Federated Learning with Partial Model Personalizationcited by 114
  13. 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Samplingcited by 66
  14. 2020A Framework for Evaluating Gradient Leakage Attacks in Federated Learningcited by 64
  15. 2017Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-offs by Selective Executioncited by 59
  16. 2021Personalized Federated Learning through Local Memorizationcited by 57
  17. 2022FedAvg with Fine Tuning: Local Updates Lead to Representation Learningcited by 56
  18. 2021Gradient-Leakage Resilient Federated Learningcited by 40
  19. 2022Federated Learning under Distributed Concept Driftcited by 36
  20. 2021Source Inference Attacks in Federated Learningcited by 34
  21. 2022Motley: Benchmarking Heterogeneity and Personalization in Federated Learningcited by 31
  22. 2017Neural Speed Reading via Skim-RNNcited by 14

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 1.

6 papers.

  1. 2024Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Modelscited by 49
  2. 2025HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Modelscited by 31
  3. 2024DualGFL: Federated Learning with a Dual-Level Coalition-Auction Gamecited by 5
  4. 2024FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillationcited by 3
  5. 2025Personalized Subgraph Federated Learning with Sheaf Collaborationcited by 1
  6. 2026From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learningcited by 0