# TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning

arXiv: https://arxiv.org/abs/2501.05053 · HTML: https://mlsanity.com/paper/2501.05053/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 48 references that are in the map.
- First posted on arXiv 9 Jan 2025.

## Builds on (most cited first, 20 of 20)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks](https://mlsanity.com/paper/1802.08232/) (2018)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Differentially Private Federated Learning: A Client Level Perspective](https://mlsanity.com/paper/1712.07557/) (2017)
- [A Hybrid Approach to Privacy-Preserving Federated Learning](https://mlsanity.com/paper/1812.03224/) (2018)
- [Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning](https://mlsanity.com/paper/2002.04156/) (2020)
- [FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning](https://mlsanity.com/paper/2009.11248/) (2020)
- [HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning](https://mlsanity.com/paper/1912.05897/) (2019)
- [FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data](https://mlsanity.com/paper/2103.03918/) (2021)
- [EIFFeL: Ensuring Integrity for Federated Learning](https://mlsanity.com/paper/2112.12727/) (2021)
- [RoFL: Robustness of Secure Federated Learning](https://mlsanity.com/paper/2107.03311/) (2021)
- [Efficient Dropout-resilient Aggregation for Privacy-preserving Machine Learning](https://mlsanity.com/paper/2203.17044/) (2022)
- [Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning](https://mlsanity.com/paper/2308.09883/) (2023)
- [CryptoNN: Training Neural Networks over Encrypted Data](https://mlsanity.com/paper/1904.07303/) (2019)
- [DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Setting](https://mlsanity.com/paper/2207.07779/) (2022)
- [Separation of Powers in Federated Learning](https://mlsanity.com/paper/2105.09400/) (2021)
- [NN-EMD: Efficiently Training Neural Networks using Encrypted Multi-Sourced Datasets](https://mlsanity.com/paper/2012.10547/) (2020)
- [Revisiting Secure Computation Using Functional Encryption: Opportunities and Research Directions](https://mlsanity.com/paper/2011.06191/) (2020)

## Built on by (most cited first, 4 of 4)

- [Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.19101/) (2025)
- [Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise](https://mlsanity.com/paper/2509.10385/) (2025)
- [Privacy-Preserving Federated Learning from Partial Decryption Verifiable Threshold Multi-Client Functional Encryption](https://mlsanity.com/paper/2511.12936/) (2025)
- [PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning](https://mlsanity.com/paper/2607.06612/) (2026)

## Abstract (arXiv)

Federated learning is a computing paradigm that enhances privacy by enabling multiple parties to collaboratively train a machine learning model without revealing personal data. However, current research indicates that traditional federated learning platforms are unable to ensure privacy due to privacy leaks caused by the interchange of gradients. To achieve privacy-preserving federated learning, integrating secure aggregation mechanisms is essential. Unfortunately, existing solutions are vulnerable to recently demonstrated inference attacks such as the disaggregation attack. This paper proposes TAPFed, an approach for achieving privacy-preserving federated learning in the context of multiple decentralized aggregators with malicious actors. TAPFed uses a proposed threshold functional encryption scheme and allows for a certain number of malicious aggregators while maintaining security and privacy. We provide formal security and privacy analyses of TAPFed and compare it to various baselines through experimental evaluation. Our results show that TAPFed offers equivalent performance in terms of model quality compared to state-of-the-art approaches while reducing transmission overhead by 29%-45% across different model training scenarios. Most importantly, TAPFed can defend against recently demonstrated inference attacks caused by curious aggregators, which the majority of existing approaches are susceptible to.