mlsanityData to 14 Oct 2026

BGTplanner: Maximizing Training Accuracy for Differentially Private Federated Recommenders via Strategic Privacy Budget Allocation

arXiv 2412.02934 ↗First posted 4 Dec 2024cs.LG cs.DCcited by 1

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Abstract (arXiv)

To mitigate the rising concern about privacy leakage, the federated recommender (FR) paradigm emerges, in which decentralized clients co-train the recommendation model without exposing their raw user-item rating data. The differentially private federated recommender (DPFR) further enhances FR by injecting differentially private (DP) noises into clients. Yet, current DPFRs, suffering from noise distortion, cannot achieve satisfactory accuracy. Various efforts have been dedicated to improving DPFRs by adaptively allocating the privacy budget over the learning process. However, due to the intricate relation between privacy budget allocation and model accuracy, existing works are still far from maximizing DPFR accuracy. To address this challenge, we develop BGTplanner (Budget Planner) to strategically allocate the privacy budget for each round of DPFR training, improving overall training performance. Specifically, we leverage the Gaussian process regression and historical information to predict the change in recommendation accuracy with a certain allocated privacy budget. Additionally, Contextual Multi-Armed Bandit (CMAB) is harnessed to make privacy budget allocation decisions by reconciling the current improvement and long-term privacy constraints. Our extensive experimental results on real datasets demonstrate that BGTplanner achieves an average improvement of 6.76% in training performance compared to state-of-the-art baselines.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 25 of its 72 references that are in the map. First posted on arXiv 4 Dec 2024.

Builds on

25 of 72 references.

References · 25

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2016Deep Learning with Differential Privacycited by 2,728
  4. 2019Introduction to Online Convex Optimizationcited by 803
  5. 2017Renyi Differential Privacycited by 650
  6. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  7. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 520
  8. 2021Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learningcited by 118
  9. 2021FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendationcited by 98
  10. 2017Geometric Matrix Completion with Recurrent Multi-Graph Neural Networkscited by 88
  11. 2015Linear Contextual Bandits with Knapsackscited by 82
  12. 2021Federated Multi-armed Bandits with Personalizationcited by 62
13 more
  1. 2015Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Banditscited by 29
  2. 2022Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendationcited by 22
  3. 2023No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendationcited by 18
  4. 2023Active Membership Inference Attack under Local Differential Privacy in Federated Learningcited by 17
  5. 2021On the Renyi Differential Privacy of the Shuffle Modelcited by 16
  6. 2022Adap DP-FL: Differentially Private Federated Learning with Adaptive Noisecited by 13
  7. 2023Task-Agnostic Privacy-Preserving Representation Learning for Federated Learning Against Attribute Inference Attackscited by 13
  8. 2022Optimal Contextual Bandits with Knapsacks under Realizability via Regression Oraclescited by 12
  9. 2023Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approachcited by 8
  10. 2022PrivateRec: Differentially Private Training and Serving for Federated News Recommendationcited by 6
  11. 2021Dynamic Privacy Budget Allocation Improves Data Efficiency of Differentially Private Gradient Descentcited by 5
  12. 2023BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learningcited by 5
  13. 2021Gain without Pain: Offsetting DP-injected Nosies Stealthily in Cross-device Federated Learningcited by 3

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Citing papers per year: 2026: 1.

1 paper.

  1. 2026Adapter-Augmented Bandits for Online Multi-Constrained Multi-Modal Inference Schedulingcited by 2