# Byzantine-Robust and Privacy-Preserving Framework for FedML

arXiv: https://arxiv.org/abs/2105.02295 · HTML: https://mlsanity.com/paper/2105.02295/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 5 May 2021.

## Builds on (most cited first, 16 of 16)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (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)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [Analyzing Federated Learning through an Adversarial Lens](https://mlsanity.com/paper/1811.12470/) (2018)
- [The Hidden Vulnerability of Distributed Learning in Byzantium](https://mlsanity.com/paper/1802.07927/) (2018)
- [Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent](https://mlsanity.com/paper/1705.05491/) (2017)
- [A Scalable Approach for Privacy-Preserving Collaborative Machine Learning](https://mlsanity.com/paper/2011.01963/) (2020)
- [Dopamine: Differentially Private Federated Learning on Medical Data](https://mlsanity.com/paper/2101.11693/) (2021)
- [Byzantine-Resilient Stochastic Gradient Descent for Distributed Learning: A Lipschitz-Inspired Coordinate-wise Median Approach](https://mlsanity.com/paper/1909.04532/) (2019)
- [Byzantine-Robust Decentralized Stochastic Optimization over Static and Time-Varying Networks](https://mlsanity.com/paper/2005.06276/) (2020)
- [Secure multiparty computations in floating-point arithmetic](https://mlsanity.com/paper/2001.03192/) (2020)

## Built on by (most cited first, 14 of 14)

- [Eluding Secure Aggregation in Federated Learning via Model Inconsistency](https://mlsanity.com/paper/2111.07380/) (2021)
- [Privacy-Preserving Machine Learning: Methods, Challenges and Directions](https://mlsanity.com/paper/2108.04417/) (2021)
- [CrowdGuard: Federated Backdoor Detection in Federated Learning](https://mlsanity.com/paper/2210.07714/) (2022)
- [DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardware](https://mlsanity.com/paper/2207.00083/) (2022)
- [Attribute Inference Attack of Speech Emotion Recognition in Federated Learning Settings](https://mlsanity.com/paper/2112.13416/) (2021)
- [FLTracer: Accurate Poisoning Attack Provenance in Federated Learning](https://mlsanity.com/paper/2310.13424/) (2023)
- [Adaptive Verifiable Coded Computing: Towards Fast, Secure and Private Distributed Machine Learning](https://mlsanity.com/paper/2107.12958/) (2021)
- [MPC-Pipe: an Efficient Pipeline Scheme for Secure Multi-party Machine Learning Inference](https://mlsanity.com/paper/2209.13643/) (2022)
- [FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks](https://mlsanity.com/paper/2310.02113/) (2023)
- [FLAIRS: FPGA-Accelerated Inference-Resistant & Secure Federated Learning](https://mlsanity.com/paper/2308.00553/) (2023)
- [Memory Backdoor Attacks on Neural Networks](https://mlsanity.com/paper/2411.14516/) (2024)
- [Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations](https://mlsanity.com/paper/2306.03600/) (2023)
- [ERIS: Enhancing the Privacy-Utility-Scalability Trade-Off in Federated Learning via Federated Shard Aggregation](https://mlsanity.com/paper/2602.08617/) (2026)
- [Trustful LLMs: Customizing and Grounding Text Generation with Knowledge Bases and Dual Decoders](https://mlsanity.com/paper/2411.07870/) (2024)

## Abstract (arXiv)

Federated learning has emerged as a popular paradigm for collaboratively training a model from data distributed among a set of clients. This learning setting presents, among others, two unique challenges: how to protect privacy of the clients' data during training, and how to ensure integrity of the trained model. We propose a two-pronged solution that aims to address both challenges under a single framework. First, we propose to create secure enclaves using a trusted execution environment (TEE) within the server. Each client can then encrypt their gradients and send them to verifiable enclaves. The gradients are decrypted within the enclave without the fear of privacy breaches. However, robustness check computations in a TEE are computationally prohibitive. Hence, in the second step, we perform a novel gradient encoding that enables TEEs to encode the gradients and then offloading Byzantine check computations to accelerators such as GPUs. Our proposed approach provides theoretical bounds on information leakage and offers a significant speed-up over the baseline in empirical evaluation.