# Privacy-Preserving Aggregation in Federated Learning: A Survey

arXiv: https://arxiv.org/abs/2203.17005 · HTML: https://mlsanity.com/paper/2203.17005/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2022.

## Built on by (most cited first, 27 of 27)

- [A Survey on Federated Unlearning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/2310.20448/) (2023)
- [Threats, Attacks, and Defenses in Machine Unlearning: A Survey](https://mlsanity.com/paper/2403.13682/) (2024)
- [FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models](https://mlsanity.com/paper/2304.13407/) (2023)
- [Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning](https://mlsanity.com/paper/2401.08216/) (2024)
- [Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things](https://mlsanity.com/paper/2402.02506/) (2024)
- [A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures](https://mlsanity.com/paper/2404.00673/) (2024)
- [SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning](https://mlsanity.com/paper/2205.09986/) (2022)
- [Privacy-Preserving Federated Unlearning with Certified Client Removal](https://mlsanity.com/paper/2404.09724/) (2024)
- [Trustworthy Federated Learning: A Survey](https://mlsanity.com/paper/2305.11537/) (2023)
- [Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives](https://mlsanity.com/paper/2307.02140/) (2023)
- [FedUHB: Accelerating Federated Unlearning via Polyak Heavy Ball Method](https://mlsanity.com/paper/2411.11039/) (2024)
- [User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networks](https://mlsanity.com/paper/2309.09253/) (2023)
- [Harnessing the Power of Federated Learning in Federated Contextual Bandits](https://mlsanity.com/paper/2312.16341/) (2023)
- [Privacy-preserving gradient-based fair federated learning](https://mlsanity.com/paper/2407.13881/) (2024)
- [Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions](https://mlsanity.com/paper/2503.10058/) (2025)
- [Integrating Identity-Based Identification against Adaptive Adversaries in Federated Learning](https://mlsanity.com/paper/2504.03077/) (2025)
- [ModShift: Model Privacy via Designed Shifts](https://mlsanity.com/paper/2507.20060/) (2025)
- [TOFA: Training-Free One-Shot Federated Adaptation for Vision-Language Models](https://mlsanity.com/paper/2511.16423/) (2025)
- [Setup Once, Secure Always: A Single-Setup Secure Federated Learning Aggregation Protocol with Forward and Backward Secrecy for Dynamic Users](https://mlsanity.com/paper/2502.08989/) (2025)
- [Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AI](https://mlsanity.com/paper/2503.16233/) (2025)
- [Revealing the influence of participant failures on model quality in cross-silo Federated Learning](https://mlsanity.com/paper/2603.25289/) (2026)
- [Privacy-Preserving Distributed Learning in IoT Systems: A Unified Threat Model and Evaluation Framework](https://mlsanity.com/paper/2605.09232/) (2026)
- [The Capacity of Information-Theoretic Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2606.07277/) (2026)
- [Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems](https://mlsanity.com/paper/2606.25533/) (2026)
- [MaxModShift: Model Privacy via Designed Shifts](https://mlsanity.com/paper/2608.09328/) (2026)
- [Decentralized Gossip Learning and Federated Averaging for Histopathology Image Classification](https://mlsanity.com/paper/2609.16448/) (2026)
- [Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunities](https://mlsanity.com/paper/2609.28722/) (2026)

## 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.