# FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning

arXiv: https://arxiv.org/abs/2508.10042 · HTML: https://mlsanity.com/paper/2508.10042/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Federated unlearning and poisoning, one of 12 directions in LLM safety and security.
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 11 Aug 2025.

## Placed in

- LLM safety and security / [Federated unlearning and poisoning](https://mlsanity.com/safety/federated-unlearning-and-poisoning/)

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

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Robust Aggregation for Federated Learning](https://mlsanity.com/paper/1912.13445/) (2019)
- [Data Poisoning Attacks Against Federated Learning Systems](https://mlsanity.com/paper/2007.08432/) (2020)
- [Revisiting Weighted Aggregation in Federated Learning with Neural Networks](https://mlsanity.com/paper/2302.10911/) (2023)
- [Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation](https://mlsanity.com/paper/2101.06905/) (2021)
- [BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecture](https://mlsanity.com/paper/2010.07427/) (2020)
- [Trustworthy Federated Learning via Blockchain](https://mlsanity.com/paper/2209.04418/) (2022)
- [Untargeted Poisoning Attack Detection in Federated Learning via Behavior Attestation](https://mlsanity.com/paper/2101.10904/) (2021)
- [HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection](https://mlsanity.com/paper/2204.04254/) (2022)
- [Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications](https://mlsanity.com/paper/2207.08486/) (2022)
- [Recursive Euclidean Distance Based Robust Aggregation Technique For Federated Learning](https://mlsanity.com/paper/2303.11337/) (2023)

## Abstract (arXiv)

Federated learning enhances traditional deep learning by enabling the joint training of a model with the use of IoT device's private data. It ensures privacy for clients, but is susceptible to data poisoning attacks during training that degrade model performance and integrity. Current poisoning detection methods in federated learning lack a standardized detection method or take significant liberties with trust. In this paper, we present Sys, a novel blockchain-enabled poison detection framework in federated learning. The framework decentralizes the role of the global server across participating clients. We introduce a judge model used to detect data poisoning in model updates. The judge model is produced by each client and verified to reach consensus on a single judge model. We implement our solution to show Sys is robust against data poisoning attacks and the creation of our judge model is scalable.