mlsanityData to 14 Oct 2026

Personalized Online Federated Learning with Multiple Kernels

arXiv 2311.05108 ↗First posted 9 Nov 2023cs.LGcited by 11

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Abstract (arXiv)

Multi-kernel learning (MKL) exhibits well-documented performance in online non-linear function approximation. Federated learning enables a group of learners (called clients) to train an MKL model on the data distributed among clients to perform online non-linear function approximation. There are some challenges in online federated MKL that need to be addressed: i) Communication efficiency especially when a large number of kernels are considered ii) Heterogeneous data distribution among clients. The present paper develops an algorithmic framework to enable clients to communicate with the server to send their updates with affordable communication cost while clients employ a large dictionary of kernels. Utilizing random feature (RF) approximation, the present paper proposes scalable online federated MKL algorithm. We prove that using the proposed online federated MKL algorithm, each client enjoys sub-linear regret with respect to the RF approximation of its best kernel in hindsight, which indicates that the proposed algorithm can effectively deal with heterogeneity of the data distributed among clients. Experimental results on real datasets showcase the advantages of the proposed algorithm compared with other online federated kernel learning ones.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 26 of its 60 references that are in the map. First posted on arXiv 9 Nov 2023.

Builds on

26 of 60 references.

References · 26

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  3. 2019Advances and Open Problems in Federated Learningcited by 3,046
  4. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  5. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  6. 2016HyperNetworkscited by 1,027
  7. 2019Deep Leakage from Gradientscited by 1,021
  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. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 571
14 more
  1. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
  2. 2020Adaptive Personalized Federated Learningcited by 358
  3. 2021Personalized Federated Learning using Hypernetworkscited by 212
  4. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 192
  5. 2020FetchSGD: Communication-Efficient Federated Learning with Sketchingcited by 179
  6. 2016Generalization Properties of Learning with Random Featurescited by 164
  7. 2020Lower Bounds and Optimal Algorithms for Personalized Federated Learningcited by 123
  8. 2021Parameterized Knowledge Transfer for Personalized Federated Learningcited by 109
  9. 2021Personalized Federated Learning with Gaussian Processescited by 76
  10. 2019Variational Federated Multi-Task Learningcited by 69
  11. 2017Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamicscited by 28
  12. 2021Communication-Efficient Online Federated Learning Framework for Nonlinear Regressioncited by 16
  13. 2017Online Distributed Learning Over Networks in RKH Spaces Using Random Fourier Featurescited by 11
  14. 2022Graph-Assisted Communication-Efficient Ensemble Federated Learningcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 7; 2025: 3; 2026: 1.

11 papers.

  1. 2024Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuningcited by 8
  2. 2024On the Necessity of Collaboration for Online Model Selection with Decentralized Datacited by 6
  3. 2024Review of Mathematical Optimization in Federated Learningcited by 3
  4. 2024Geometrically Inspired Kernel Machines for Collaborative Learning Beyond Gradient Descentcited by 2
  5. 2025Operator-Theoretic Framework for Gradient-Free Federated Learningcited by 2
  6. 2024Budgeted Online Model Selection and Fine-Tuning via Federated Learningcited by 1
  7. 2024Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-upscited by 1
  8. 2024Towards Client Driven Federated Learningcited by 0
  9. 2025Bayesian Federated Cause-of-Death Classification and Quantification Under Distribution Shiftcited by 0
  10. 2025A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directionscited by 0
  11. 2026Adaptive Personalized Federated Learning via Multi-task Averaging of Kernel Mean Embeddingscited by 0