# Sentinel: An Aggregation Function to Secure Decentralized Federated Learning

arXiv: https://arxiv.org/abs/2310.08097 · HTML: https://mlsanity.com/paper/2310.08097/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 12 Oct 2023.

## Builds on (most cited first, 11 of 11)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping](https://mlsanity.com/paper/2012.13995/) (2020)
- [The Hidden Vulnerability of Distributed Learning in Byzantium](https://mlsanity.com/paper/1802.07927/) (2018)
- [Robust Aggregation for Federated Learning](https://mlsanity.com/paper/1912.13445/) (2019)
- [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022)
- [FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients](https://mlsanity.com/paper/2207.09209/) (2022)
- [Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges](https://mlsanity.com/paper/2201.08135/) (2022)
- [Fedstellar: A Platform for Decentralized Federated Learning](https://mlsanity.com/paper/2306.09750/) (2023)
- [FederatedTrust: A Solution for Trustworthy Federated Learning](https://mlsanity.com/paper/2302.09844/) (2023)

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

- [On the Conflict of Robustness and Learning in Collaborative Machine Learning](https://mlsanity.com/paper/2402.13700/) (2024)
- [FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks](https://mlsanity.com/paper/2508.18737/) (2025)
- [Byzantine-Resilient Federated Learning via QUBO-Based Client Selection on Quantum Annealers](https://mlsanity.com/paper/2605.16438/) (2026)
- [CACTUS: Mask-Guided Semantic Clean-Label Backdoors in Decentralized Federated Learning](https://mlsanity.com/paper/2609.02450/) (2026)

## Abstract (arXiv)

Decentralized Federated Learning (DFL) emerges as an innovative paradigm to train collaborative models, addressing the single point of failure limitation. However, the security and trustworthiness of FL and DFL are compromised by poisoning attacks, negatively impacting its performance. Existing defense mechanisms have been designed for centralized FL and they do not adequately exploit the particularities of DFL. Thus, this work introduces Sentinel, a defense strategy to counteract poisoning attacks in DFL. Sentinel leverages the accessibility of local data and defines a three-step aggregation protocol consisting of similarity filtering, bootstrap validation, and normalization to safeguard against malicious model updates. Sentinel has been evaluated with diverse datasets and data distributions. Besides, various poisoning attack types and threat levels have been verified. The results improve the state-of-the-art performance against both untargeted and targeted poisoning attacks when data follows an IID (Independent and Identically Distributed) configuration. Besides, under non-IID configuration, it is analyzed how performance degrades both for Sentinel and other state-of-the-art robust aggregation methods.