# Tazza: Shuffling Neural Network Parameters for Secure and Private Federated Learning

arXiv: https://arxiv.org/abs/2412.07454 · HTML: https://mlsanity.com/paper/2412.07454/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 75 references that are in the map.
- First posted on arXiv 10 Dec 2024.

## Builds on (most cited first, 30 of 34)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [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)
- [Domain Generalization by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1903.06864/) (2019)
- [Intriguing Properties of Vision Transformers](https://mlsanity.com/paper/2105.10497/) (2021)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping](https://mlsanity.com/paper/2012.13995/) (2020)
- [Client-Edge-Cloud Hierarchical Federated Learning](https://mlsanity.com/paper/1905.06641/) (2019)
- [Evaluating Gradient Inversion Attacks and Defenses in Federated Learning](https://mlsanity.com/paper/2112.00059/) (2021)
- [Generalized Byzantine-tolerant SGD](https://mlsanity.com/paper/1802.10116/) (2018)
- [PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments](https://mlsanity.com/paper/2104.14380/) (2021)
- [NoisyTune: A Little Noise Can Help You Finetune Pretrained Language Models Better](https://mlsanity.com/paper/2202.12024/) (2022)
- [Parallel Multi Channel Convolution using General Matrix Multiplication](https://mlsanity.com/paper/1704.04428/) (2017)
- [JigsawGAN: Auxiliary Learning for Solving Jigsaw Puzzles with Generative Adversarial Networks](https://mlsanity.com/paper/2101.07555/) (2021)
- [Differential Privacy in the Shuffle Model: A Survey of Separations](https://mlsanity.com/paper/2107.11839/) (2021)
- [FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks](https://mlsanity.com/paper/2308.05832/) (2023)
- [ADMarker: A Multi-Modal Federated Learning System for Monitoring Digital Biomarkers of Alzheimer's Disease](https://mlsanity.com/paper/2310.15301/) (2023)
- [Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight Generation](https://mlsanity.com/paper/2407.03086/) (2024)
- [FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things](https://mlsanity.com/paper/2310.00109/) (2023)
- [DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learning](https://mlsanity.com/paper/2411.12220/) (2024)

## Built on by (most cited first, 2 of 2)

- [Temperature Scaling Attack Disrupting Model Confidence in Federated Learning](https://mlsanity.com/paper/2602.06638/) (2026)
- [MPT: Missing Prototype Tracking via Barycentric Reconstruction in Vehicular Federated Learning](https://mlsanity.com/paper/2609.12771/) (2026)

## Abstract (arXiv)

Federated learning enables decentralized model training without sharing raw data, preserving data privacy. However, its vulnerability towards critical security threats, such as gradient inversion and model poisoning by malicious clients, remain unresolved. Existing solutions often address these issues separately, sacrificing either system robustness or model accuracy. This work introduces Tazza, a secure and efficient federated learning framework that simultaneously addresses both challenges. By leveraging the permutation equivariance and invariance properties of neural networks via weight shuffling and shuffled model validation, Tazza enhances resilience against diverse poisoning attacks, while ensuring data confidentiality and high model accuracy. Comprehensive evaluations on various datasets and embedded platforms show that Tazza achieves robust defense with up to 6.7x improved computational efficiency compared to alternative schemes, without compromising performance.