mlsanityData to 14 Oct 2026

LAPA-based Dynamic Privacy Optimization for Wireless Federated Learning in Heterogeneous Environments

arXiv 2505.19823 ↗First posted 26 May 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

Federated Learning (FL) is a distributed machine learning paradigm based on protecting data privacy of devices, which however, can still be broken by gradient leakage attack via parameter inversion techniques. Differential privacy (DP) technology reduces the risk of private data leakage by adding artificial noise to the gradients, but detrimental to the FL utility at the same time, especially in the scenario where the data is Non-Independent Identically Distributed (Non-IID). Based on the impact of heterogeneous data on aggregation performance, this paper proposes a Lightweight Adaptive Privacy Allocation (LAPA) strategy, which assigns personalized privacy budgets to devices in each aggregation round without transmitting any additional information beyond gradients, ensuring both privacy protection and aggregation efficiency. Furthermore, the Deep Deterministic Policy Gradient (DDPG) algorithm is employed to optimize the transmission power, in order to determine the optimal timing at which the adaptively attenuated artificial noise aligns with the communication noise, enabling an effective balance between DP and system utility. Finally, a reliable aggregation strategy is designed by integrating communication quality and data distribution characteristics, which improves aggregation performance while preserving privacy. Experimental results demonstrate that the personalized noise allocation and dynamic optimization strategy based on LAPA proposed in this paper enhances convergence performance while satisfying the privacy requirements of FL.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 37 references that are in the map. First posted on arXiv 26 May 2025.

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References · 19

  1. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  2. 2016Deep Learning with Differential Privacycited by 2,728
  3. 2019Deep Leakage from Gradientscited by 1,021
  4. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
  5. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  6. 2018ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Modelscited by 498
  7. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 459
  8. 2020iDLG: Improved Deep Leakage from Gradientscited by 382
  9. 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 334
  10. 2021See through Gradients: Image Batch Recovery via GradInversioncited by 326
  11. 2020Systematic Evaluation of Privacy Risks of Machine Learning Modelscited by 292
  12. 2018Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learningcited by 246
7 more
  1. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 210
  2. 2017Machine Learning Models that Remember Too Muchcited by 169
  3. 2021Federated Learning for Open Bankingcited by 141
  4. 2021Practical Blind Membership Inference Attack via Differential Comparisonscited by 78
  5. 2020Fast-Convergent Federated Learning with Adaptive Weightingcited by 67
  6. 2020Training Keyword Spotting Models on Non-IID Data with Federated Learningcited by 36
  7. 2020Improving on-device speaker verification using federated learning with privacycited by 30

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  2. 2026Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacy
  3. 2021Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation
  4. 2022Optimal Privacy Preserving for Federated Learning in Mobile Edge Computing
  5. 2025Optimizing Communication and Device Clustering for Clustered Federated Learning with Differential Privacy
  6. 2022Federated Stochastic Primal-dual Learning with Differential Privacy
  7. 2023Using Decentralized Aggregation for Federated Learning with Differential Privacy
  8. 2022Differentially Private Federated Learning via Reconfigurable Intelligent Surface