# TrustFed: A Reliable Federated Learning Framework with Malicious-Attack Resistance

arXiv: https://arxiv.org/abs/2312.04597 · HTML: https://mlsanity.com/paper/2312.04597/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 60 references that are in the map.
- First posted on arXiv 6 Dec 2023.

## Builds on (most cited first, 21 of 21)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [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)
- [Deep Recurrent Q-Learning for Partially Observable MDPs](https://mlsanity.com/paper/1507.06527/) (2015)
- [Data Poisoning Attacks Against Federated Learning Systems](https://mlsanity.com/paper/2007.08432/) (2020)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2009.13012/) (2020)
- [Deep Variational Reinforcement Learning for POMDPs](https://mlsanity.com/paper/1806.02426/) (2018)
- [Free-rider Attacks on Model Aggregation in Federated Learning](https://mlsanity.com/paper/2006.11901/) (2020)
- [BaFFLe: Backdoor detection via Feedback-based Federated Learning](https://mlsanity.com/paper/2011.02167/) (2020)
- [Free-riders in Federated Learning: Attacks and Defenses](https://mlsanity.com/paper/1911.12560/) (2019)
- [Policy Design for Active Sequential Hypothesis Testing using Deep Learning](https://mlsanity.com/paper/1810.04859/) (2018)
- [Deep Actor-Critic Reinforcement Learning for Anomaly Detection](https://mlsanity.com/paper/1908.10755/) (2019)
- [Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges](https://mlsanity.com/paper/2111.07392/) (2021)
- [Fixed-horizon Active Hypothesis Testing](https://mlsanity.com/paper/1911.06912/) (2019)
- [Resource Consumption for Supporting Federated Learning in Wireless Networks](https://mlsanity.com/paper/2204.03850/) (2022)
- [Active Anomaly Detection with Switching Cost](https://mlsanity.com/paper/1810.11800/) (2018)

## Built on by (most cited first, 1 of 1)

- [Pigeon-SL: Robust Split Learning Framework for Edge Intelligence under Malicious Clients](https://mlsanity.com/paper/2508.02235/) (2025)

## Abstract (arXiv)

As a key technology in 6G research, federated learning (FL) enables collaborative learning among multiple clients while ensuring individual data privacy. However, malicious attackers among the participating clients can intentionally tamper with the training data or the trained model, compromising the accuracy and trustworthiness of the system. To address this issue, in this paper, we propose a hierarchical audit-based FL (HiAudit-FL) framework, with the aim to enhance the reliability and security of the learning process. The hierarchical audit process includes two stages, namely model-audit and parameter-audit. In the model-audit stage, a low-overhead audit method is employed to identify suspicious clients. Subsequently, in the parameter-audit stage, a resource-consuming method is used to detect all malicious clients with higher accuracy among the suspicious ones. Specifically, we execute the model audit method among partial clients for multiple rounds, which is modeled as a partial observation Markov decision process (POMDP) with the aim to enhance the robustness and accountability of the decision-making in complex and uncertain environments. Meanwhile, we formulate the problem of identifying malicious attackers through a multi-round audit as an active sequential hypothesis testing problem and leverage a diffusion model-based AI-Enabled audit selection strategy (ASS) to decide which clients should be audited in each round. To accomplish efficient and effective audit selection, we design a DRL-ASS algorithm by incorporating the ASS in a deep reinforcement learning (DRL) framework. Our simulation results demonstrate that HiAudit-FL can effectively identify and handle potential malicious users accurately, with small system overhead.