# RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation

arXiv: https://arxiv.org/abs/2405.15182 · HTML: https://mlsanity.com/paper/2405.15182/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 57 references that are in the map.
- First posted on arXiv 24 May 2024.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [Exploiting Unintended Feature Leakage in Collaborative Learning](https://mlsanity.com/paper/1805.04049/) (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)
- [No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data](https://mlsanity.com/paper/2106.05001/) (2021)
- [The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation](https://mlsanity.com/paper/2102.06387/) (2021)
- [OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning](https://mlsanity.com/paper/2402.06954/) (2024)
- [Secure Federated Matrix Factorization](https://mlsanity.com/paper/1906.05108/) (2019)
- [Byzantine-Resilient Secure Federated Learning](https://mlsanity.com/paper/2007.11115/) (2020)
- [Local and Central Differential Privacy for Robustness and Privacy in Federated Learning](https://mlsanity.com/paper/2009.03561/) (2020)
- [FedDisco: Federated Learning with Discrepancy-Aware Collaboration](https://mlsanity.com/paper/2305.19229/) (2023)
- [Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learning](https://mlsanity.com/paper/2112.14468/) (2021)
- [Collecting and Analyzing Data from Smart Device Users with Local Differential Privacy](https://mlsanity.com/paper/1606.05053/) (2016)
- [RoFL: Robustness of Secure Federated Learning](https://mlsanity.com/paper/2107.03311/) (2021)
- [Secure Federated Learning against Model Poisoning Attacks via Client Filtering](https://mlsanity.com/paper/2304.00160/) (2023)
- [Fake It Till Make It: Federated Learning with Consensus-Oriented Generation](https://mlsanity.com/paper/2312.05966/) (2023)
- [On the Communication Complexity of Secure Computation](https://mlsanity.com/paper/1311.7584/) (2013)

## Built on by (most cited first, 10 of 10)

- [FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models](https://mlsanity.com/paper/2406.04845/) (2024)
- [Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models](https://mlsanity.com/paper/2406.10630/) (2024)
- [Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration](https://mlsanity.com/paper/2504.00282/) (2025)
- [Capture Global Feature Statistics for One-Shot Federated Learning](https://mlsanity.com/paper/2503.06962/) (2025)
- [Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learning](https://mlsanity.com/paper/2506.09674/) (2025)
- [How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution](https://mlsanity.com/paper/2411.10673/) (2024)
- [FLTG: Byzantine-Robust Federated Learning via Angle-Based Defense and Non-IID-Aware Weighting](https://mlsanity.com/paper/2505.12851/) (2025)
- [Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation](https://mlsanity.com/paper/2510.00976/) (2025)
- [LSHFed: Robust and Communication-Efficient Federated Learning with Locally-Sensitive Hashing Gradient Mapping](https://mlsanity.com/paper/2511.01296/) (2025)
- [Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries](https://mlsanity.com/paper/2607.20890/) (2026)

## Abstract (arXiv)

Federated learning (FL) allows multiple devices to train a model collaboratively without sharing their data. Despite its benefits, FL is vulnerable to privacy leakage and poisoning attacks. To address the privacy concern, secure aggregation (SecAgg) is often used to obtain the aggregation of gradients on sever without inspecting individual user updates. Unfortunately, existing defense strategies against poisoning attacks rely on the analysis of local updates in plaintext, making them incompatible with SecAgg. To reconcile the conflicts, we propose a robust federated learning framework against poisoning attacks (RFLPA) based on SecAgg protocol. Our framework computes the cosine similarity between local updates and server updates to conduct robust aggregation. Furthermore, we leverage verifiable packed Shamir secret sharing to achieve reduced communication cost of O(M+N) per user, and design a novel dot product aggregation algorithm to resolve the issue of increased information leakage. Our experimental results show that RFLPA significantly reduces communication and computation overhead by over 75% compared to the state-of-the-art secret sharing method, BREA, while maintaining competitive accuracy.