# Efficient Dropout-resilient Aggregation for Privacy-preserving Machine Learning

arXiv: https://arxiv.org/abs/2203.17044 · HTML: https://mlsanity.com/paper/2203.17044/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2022.

## Built on by (most cited first, 20 of 20)

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- [TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning](https://mlsanity.com/paper/2501.05053/) (2025)
- [FedUHB: Accelerating Federated Unlearning via Polyak Heavy Ball Method](https://mlsanity.com/paper/2411.11039/) (2024)
- [Recoverable Privacy-Preserving Image Classification through Noise-like Adversarial Examples](https://mlsanity.com/paper/2310.12707/) (2023)
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- [Human-imperceptible, Machine-recognizable Images](https://mlsanity.com/paper/2306.03679/) (2023)
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- [Privacy-Preserving Orthogonal Aggregation for Guaranteeing Gender Fairness in Federated Recommendation](https://mlsanity.com/paper/2411.19678/) (2024)
- [The Capacity of Information-Theoretic Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2606.07277/) (2026)

## Abstract (arXiv)

With the increasing adoption of data-hungry machine learning algorithms, personal data privacy has emerged as one of the key concerns that could hinder the success of digital transformation. As such, Privacy-Preserving Machine Learning (PPML) has received much attention from both academia and industry. However, organizations are faced with the dilemma that, on the one hand, they are encouraged to share data to enhance ML performance, but on the other hand, they could potentially be breaching the relevant data privacy regulations. Practical PPML typically allows multiple participants to individually train their ML models, which are then aggregated to construct a global model in a privacy-preserving manner, e.g., based on multi-party computation or homomorphic encryption. Nevertheless, in most important applications of large-scale PPML, e.g., by aggregating clients' gradients to update a global model for federated learning, such as consumer behavior modeling of mobile application services, some participants are inevitably resource-constrained mobile devices, which may drop out of the PPML system due to their mobility nature. Therefore, the resilience of privacy-preserving aggregation has become an important problem to be tackled. In this paper, we propose a scalable privacy-preserving aggregation scheme that can tolerate dropout by participants at any time, and is secure against both semi-honest and active malicious adversaries by setting proper system parameters. By replacing communication-intensive building blocks with a seed homomorphic pseudo-random generator, and relying on the additive homomorphic property of Shamir secret sharing scheme, our scheme outperforms state-of-the-art schemes by up to 6.37× in runtime and provides a stronger dropout-resilience. The simplicity of our scheme makes it attractive both for implementation and for further improvements.