mlsanityData to 7 Oct 2026

Privacy-Preserving Aggregation in Federated Learning: A Survey

arXiv 2203.17005 ↗First posted 31 Mar 2022cs.CRcited by 25

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Abstract (arXiv)

Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms and growing concerns over personal data privacy, Privacy-Preserving Federated Learning (PPFL) has attracted tremendous attention from both academia and industry. Practical PPFL typically allows multiple participants to individually train their machine learning models, which are then aggregated to construct a global model in a privacy-preserving manner. As such, Privacy-Preserving Aggregation (PPAgg) as the key protocol in PPFL has received substantial research interest. This survey aims to fill the gap between a large number of studies on PPFL, where PPAgg is adopted to provide a privacy guarantee, and the lack of a comprehensive survey on the PPAgg protocols applied in FL systems. In this survey, we review the PPAgg protocols proposed to address privacy and security issues in FL systems. The focus is placed on the construction of PPAgg protocols with an extensive analysis of the advantages and disadvantages of these selected PPAgg protocols and solutions. Additionally, we discuss the open-source FL frameworks that support PPAgg. Finally, we highlight important challenges and future research directions for applying PPAgg to FL systems and the combination of PPAgg with other technologies for further security improvement.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Mar 2022.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 5; 2024: 6; 2025: 6; 2026: 7.

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  1. 2024Threats, Attacks, and Defenses in Machine Unlearning: A Surveycited by 34
  2. 2023FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Modelscited by 17
  3. 2024Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learningcited by 7
  4. 2024Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Thingscited by 6
  5. 2024A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasurescited by 6
  6. 2022SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learningcited by 5
  7. 2023Trustworthy Federated Learning: A Surveycited by 4
  8. 2023Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectivescited by 3
  9. 2024FedUHB: Accelerating Federated Unlearning via Polyak Heavy Ball Methodcited by 3
  10. 2023User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networkscited by 1
  11. 2023Harnessing the Power of Federated Learning in Federated Contextual Banditscited by 1
  12. 2024Privacy-preserving gradient-based fair federated learningcited by 1
13 more
  1. 2025Integrating Identity-Based Identification against Adaptive Adversaries in Federated Learningcited by 1
  2. 2025ModShift: Model Privacy via Designed Shiftscited by 1
  3. 2025TOFA: Training-Free One-Shot Federated Adaptation for Vision-Language Modelscited by 1
  4. 2025Setup Once, Secure Always: A Single-Setup Secure Federated Learning Aggregation Protocol with Forward and Backward Secrecy for Dynamic Userscited by 0
  5. 2025Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directionscited by 0
  6. 2025Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AIcited by 0
  7. 2026Revealing the influence of participant failures on model quality in cross-silo Federated Learningcited by 0
  8. 2026Privacy-Preserving Distributed Learning in IoT Systems: A Unified Threat Model and Evaluation Frameworkcited by 0
  9. 2026The Capacity of Information-Theoretic Secure Aggregation in Federated Learningcited by 0
  10. 2026Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systemscited by 0
  11. 2026MaxModShift: Model Privacy via Designed Shiftscited by 0
  12. 2026Decentralized Gossip Learning and Federated Averaging for Histopathology Image Classificationcited by 0
  13. 2026Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunitiescited by 0