# FLAIRS: FPGA-Accelerated Inference-Resistant & Secure Federated Learning

arXiv: https://arxiv.org/abs/2308.00553 · HTML: https://mlsanity.com/paper/2308.00553/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Federated unlearning and poisoning, one of 12 directions in LLM safety and security.
- Builds on 12 of its 47 references that are in the map.
- First posted on arXiv 1 Aug 2023.

## Placed in

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

## Builds on (most cited first, 12 of 12)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning](https://mlsanity.com/paper/1904.01067/) (2019)
- [DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection](https://mlsanity.com/paper/2201.00763/) (2022)
- [BaFFLe: Backdoor detection via Feedback-based Federated Learning](https://mlsanity.com/paper/2011.02167/) (2020)
- [FL-Defender: Combating Targeted Attacks in Federated Learning](https://mlsanity.com/paper/2207.00872/) (2022)
- [Byzantine-Robust and Privacy-Preserving Framework for FedML](https://mlsanity.com/paper/2105.02295/) (2021)
- [FPGA-Based Hardware Accelerator of Homomorphic Encryption for Efficient Federated Learning](https://mlsanity.com/paper/2007.10560/) (2020)
- [ShEF: Shielded Enclaves for Cloud FPGAs](https://mlsanity.com/paper/2103.03500/) (2021)
- [FLVoogd: Robust And Privacy Preserving Federated Learning](https://mlsanity.com/paper/2207.00428/) (2022)

## Built on by (most cited first, 3 of 3)

- [FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning](https://mlsanity.com/paper/2312.04432/) (2023)
- [SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning (Full Version)](https://mlsanity.com/paper/2501.06650/) (2025)
- [FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks](https://mlsanity.com/paper/2310.02113/) (2023)

## Abstract (arXiv)

Federated Learning (FL) has become very popular since it enables clients to train a joint model collaboratively without sharing their private data. However, FL has been shown to be susceptible to backdoor and inference attacks. While in the former, the adversary injects manipulated updates into the aggregation process; the latter leverages clients' local models to deduce their private data. Contemporary solutions to address the security concerns of FL are either impractical for real-world deployment due to high-performance overheads or are tailored towards addressing specific threats, for instance, privacy-preserving aggregation or backdoor defenses. Given these limitations, our research delves into the advantages of harnessing the FPGA-based computing paradigm to overcome performance bottlenecks of software-only solutions while mitigating backdoor and inference attacks. We utilize FPGA-based enclaves to address inference attacks during the aggregation process of FL. We adopt an advanced backdoor-aware aggregation algorithm on the FPGA to counter backdoor attacks. We implemented and evaluated our method on Xilinx VMK-180, yielding a significant speed-up of around 300 times on the IoT-Traffic dataset and more than 506 times on the CIFAR-10 dataset.