mlsanityData to 14 Oct 2026

ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations

arXiv 2308.10457 ↗First posted 21 Aug 2023cs.LG cs.CRcited by 1

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Abstract (arXiv)

Federated Learning (FL) is a distributed machine learning technique that allows model training among multiple devices or organizations by sharing training parameters instead of raw data. However, adversaries can still infer individual information through inference attacks (e.g. differential attacks) on these training parameters. As a result, Differential Privacy (DP) has been widely used in FL to prevent such attacks. We consider differentially private federated learning in a resource-constrained scenario, where both privacy budget and communication rounds are constrained. By theoretically analyzing the convergence, we can find the optimal number of local DPSGD iterations for clients between any two sequential global updates. Based on this, we design an algorithm of Differentially Private Federated Learning with Adaptive Local Iterations (ALI-DPFL). We experiment our algorithm on the MNIST, FashionMNIST and Cifar10 datasets, and demonstrate significantly better performances than previous work in the resource-constraint scenario. Code is available at https://github.com/cheng-t/ALI-DPFL.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 33 of its 52 references that are in the map. First posted on arXiv 21 Aug 2023.

Builds on

33 of 52 references.

References · 33

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2016Deep Learning with Differential Privacycited by 2,728
  5. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  6. 2020Adaptive Federated Optimizationcited by 1,046
  7. 2019Deep Leakage from Gradientscited by 1,021
  8. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  9. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 682
  10. 2017Renyi Differential Privacycited by 650
  11. 2018Local SGD Converges Fast and Communicates Littlecited by 605
  12. 2014Convex Optimization: Algorithms and Complexitycited by 575
21 more
  1. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  2. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 520
  3. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  4. 2018Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learningcited by 278
  5. 2019Tighter Theory for Local SGD on Identical and Heterogeneous Datacited by 266
  6. 2019R'enyi Differential Privacy of the Sampled Gaussian Mechanismcited by 204
  7. 2017Machine Learning Models that Remember Too Muchcited by 169
  8. 2020Understanding Gradient Clipping in Private SGD: A Geometric Perspectivecited by 142
  9. 2022Layer-wised Model Aggregation for Personalized Federated Learningcited by 96
  10. 2022Automatic Clipping: Differentially Private Deep Learning Made Easier and Strongercited by 88
  11. 2021Differentially Private Federated Learning on Heterogeneous Datacited by 74
  12. 2021Layer-wise Adaptive Model Aggregation for Scalable Federated Learningcited by 36
  13. 2022Normalized/Clipped SGD with Perturbation for Differentially Private Non-Convex Optimizationcited by 36
  14. 2022Differentially Private Learning with Per-Sample Adaptive Clippingcited by 21
  15. 2022Adap DP-FL: Differentially Private Federated Learning with Adaptive Noisecited by 13
  16. 2022Adaptive Aggregation For Federated Learningcited by 10
  17. 2021Constrained Differentially Private Federated Learning for Low-bandwidth Devicescited by 6
  18. 2023Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithmcited by 5
  19. 2023Private Federated Learning with Autotuned Compressioncited by 5
  20. 2021Layer-wise and Dimension-wise Locally Adaptive Federated Learningcited by 3
  21. 2021Optimizing the Numbers of Queries and Replies in Federated Learning with Differential Privacycited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregationcited by 0