mlsanityData to 14 Oct 2026

WW-FL: Secure and Private Large-Scale Federated Learning

arXiv 2302.09904 ↗First posted 20 Feb 2023cs.LG cs.CR cs.DCcited by 2

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Abstract (arXiv)

Federated learning (FL) is an efficient approach for large-scale distributed machine learning that promises data privacy by keeping training data on client devices. However, recent research has uncovered vulnerabilities in FL, impacting both security and privacy through poisoning attacks and the potential disclosure of sensitive information in individual model updates as well as the aggregated global model. This paper explores the inadequacies of existing FL protection measures when applied independently, and the challenges of creating effective compositions. Addressing these issues, we propose WW-FL, an innovative framework that combines secure multi-party computation (MPC) with hierarchical FL to guarantee data and global model privacy. One notable feature of WW-FL is its capability to prevent malicious clients from directly poisoning model parameters, confining them to less destructive data poisoning attacks. We furthermore provide a PyTorch-based FL implementation integrated with Meta's CrypTen MPC framework to systematically measure the performance and robustness of WW-FL. Our extensive evaluation demonstrates that WW-FL is a promising solution for secure and private large-scale federated learning.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 52 of its 138 references that are in the map. First posted on arXiv 20 Feb 2023.

Builds on

52 of 138 references.

References · 52

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2017Accurate, Large Minibatch SGD: Training ImageNet in 1 Hourcited by 1,492
  3. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  4. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,150
  5. 2019Deep Leakage from Gradientscited by 1,021
  6. 2019Towards Federated Learning at Scale: System Designcited by 832
  7. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
  8. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
  9. 2020The Future of Digital Health with Federated Learningcited by 633
  10. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 493
  11. 2018Analyzing Federated Learning through an Adversarial Lenscited by 351
  12. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 346
38 more
  1. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 309
  2. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 300
  3. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 275
  4. 2018Don't Use Large Mini-Batches, Use Local SGDcited by 202
  5. 2017A Survey on Homomorphic Encryption Schemes: Theory and Implementationcited by 156
  6. 2018RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasetscited by 149
  7. 2021CrypTen: Secure Multi-Party Computation Meets Machine Learningcited by 131
  8. 2021When the Curious Abandon Honesty: Federated Learning Is Not Privatecited by 127
  9. 2022FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clientscited by 125
  10. 2021Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learningcited by 118
  11. 2021Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Modelscited by 108
  12. 2022Neurotoxin: Durable Backdoors in Federated Learningcited by 98
  13. 2020CrypTFlow2: Practical 2-Party Secure Inferencecited by 82
  14. 2020FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learningcited by 74
  15. 2022Fishing for User Data in Large-Batch Federated Learning via Gradient Magnificationcited by 73
  16. 2018Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applicationscited by 67
  17. 2021Eluding Secure Aggregation in Federated Learning via Model Inconsistencycited by 67
  18. 2020IBM Federated Learning: an Enterprise Framework White Paper V0.1cited by 57
  19. 2021Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learningcited by 39
  20. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 35
  21. 2019SoK: Differential Privaciescited by 34
  22. 2020BLAZE: Blazing Fast Privacy-Preserving Machine Learningcited by 32
  23. 2022Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guaranteescited by 30
  24. 2019Trident: Efficient 4PC Framework for Privacy Preserving Machine Learningcited by 26
  25. 2020Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGDcited by 26
  26. 2020POSEIDON: Privacy-Preserving Federated Neural Network Learningcited by 24
  27. 2019ASTRA: High Throughput 3PC over Rings with Application to Secure Predictioncited by 18
  28. 2023Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregationcited by 16
  29. 2021Secure Quantized Training for Deep Learningcited by 15
  30. 2021H-FL: A Hierarchical Communication-Efficient and Privacy-Protected Architecture for Federated Learningcited by 14
  31. 2018Resource Management in Fog/Edge Computing: A Surveycited by 13
  32. 2020PrivFL: Practical Privacy-preserving Federated Regressions on High-dimensional Data over Mobile Networkscited by 10
  33. 2020Scalable Privacy-Preserving Distributed Learningcited by 9
  34. 2021Tetrad: Actively Secure 4PC for Secure Training and Inferencecited by 8
  35. 2022ScionFL: Efficient and Robust Secure Quantized Aggregationcited by 8
  36. 2022zPROBE: Zero Peek Robustness Checks for Federated Learningcited by 6
  37. 2022SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learningcited by 5
  38. 2022Hercules: Boosting the Performance of Privacy-preserving Federated Learningcited by 4

50 most cited of 52 All 52 (JSON)

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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025FuSeFL: Fully Secure and Scalable Federated Learningcited by 2
  2. 2025SLVR: Securely Leveraging Client Validation for Robust Federated Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Privacy in Federated Learning
  2. 2024Analysis of Privacy Leakage in Federated Large Language Models
  3. 2021PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments
  4. 2025Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation
  5. 2023FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users
  6. 2023Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning
  7. 2026FedFG: Privacy-Preserving and Robust Federated Learning via Flow-Matching Generation
  8. 2025SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning