# DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Setting

arXiv: https://arxiv.org/abs/2207.07779 · HTML: https://mlsanity.com/paper/2207.07779/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2022.

## Built on by (most cited first, 12 of 12)

- [Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation](https://mlsanity.com/paper/2406.12815/) (2024)
- [Federated Retrieval-Augmented Generation: A Systematic Mapping Study](https://mlsanity.com/paper/2505.18906/) (2025)
- [Trustworthy Federated Learning: A Survey](https://mlsanity.com/paper/2305.11537/) (2023)
- [Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural Network](https://mlsanity.com/paper/2306.10351/) (2023)
- [Enhancing Object Detection with Hybrid dataset in Manufacturing Environments: Comparing Federated Learning to Conventional Techniques](https://mlsanity.com/paper/2408.08974/) (2024)
- [TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning](https://mlsanity.com/paper/2501.05053/) (2025)
- [EncCluster: Scalable Functional Encryption in Federated Learning through Weight Clustering and Probabilistic Filters](https://mlsanity.com/paper/2406.09152/) (2024)
- [Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning](https://mlsanity.com/paper/2502.05547/) (2025)
- [Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Review](https://mlsanity.com/paper/2409.16340/) (2024)
- [DFAMS: Dynamic-flow guided Federated Alignment based Multi-prototype Search](https://mlsanity.com/paper/2508.20353/) (2025)
- [EFU: Enforcing Federated Unlearning via Functional Encryption](https://mlsanity.com/paper/2508.07873/) (2025)
- [Privacy-Preserving Federated Learning from Partial Decryption Verifiable Threshold Multi-Client Functional Encryption](https://mlsanity.com/paper/2511.12936/) (2025)

## Abstract (arXiv)

Federated learning has emerged as a privacy-preserving machine learning approach where multiple parties can train a single model without sharing their raw training data. Federated learning typically requires the utilization of multi-party computation techniques to provide strong privacy guarantees by ensuring that an untrusted or curious aggregator cannot obtain isolated replies from parties involved in the training process, thereby preventing potential inference attacks. Until recently, it was thought that some of these secure aggregation techniques were sufficient to fully protect against inference attacks coming from a curious aggregator. However, recent research has demonstrated that a curious aggregator can successfully launch a disaggregation attack to learn information about model updates of a target party. This paper presents DeTrust-FL, an efficient privacy-preserving federated learning framework for addressing the lack of transparency that enables isolation attacks, such as disaggregation attacks, during secure aggregation by assuring that parties' model updates are included in the aggregated model in a private and secure manner. DeTrust-FL proposes a decentralized trust consensus mechanism and incorporates a recently proposed decentralized functional encryption (FE) scheme in which all parties agree on a participation matrix before collaboratively generating decryption key fragments, thereby gaining control and trust over the secure aggregation process in a decentralized setting. Our experimental evaluation demonstrates that DeTrust-FL outperforms state-of-the-art FE-based secure multi-party aggregation solutions in terms of training time and reduces the volume of data transferred. In contrast to existing approaches, this is achieved without creating any trust dependency on external trusted entities.