# CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications

arXiv: https://arxiv.org/abs/2302.00271 · HTML: https://mlsanity.com/paper/2302.00271/

## 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 10 of its 26 references that are in the map.
- First posted on arXiv 1 Feb 2023.

## Placed in

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

## Builds on (most cited first, 10 of 10)

- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Exploiting Unintended Feature Leakage in Collaborative Learning](https://mlsanity.com/paper/1805.04049/) (2018)
- [Protection Against Reconstruction and Its Applications in Private Federated Learning](https://mlsanity.com/paper/1812.00984/) (2018)
- [Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2207.00427/) (2022)
- [Gradient-Leakage Resilient Federated Learning](https://mlsanity.com/paper/2107.01154/) (2021)
- [Performance Optimization for Semantic Communications: An Attention-based Reinforcement Learning Approach](https://mlsanity.com/paper/2208.08239/) (2022)
- [Gradient Leakage Attack Resilient Deep Learning](https://mlsanity.com/paper/2112.13178/) (2021)
- [Secure Aggregation for Federated Learning in Flower](https://mlsanity.com/paper/2205.06117/) (2022)
- [Secure Semantic Communications: Fundamentals and Challenges](https://mlsanity.com/paper/2301.01421/) (2023)

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

- [Recent Methodological Advances in Federated Learning for Healthcare](https://mlsanity.com/paper/2310.02874/) (2023)
- [Trustworthy Federated Learning: A Survey](https://mlsanity.com/paper/2305.11537/) (2023)
- [Few-shot Multi-domain Knowledge Rearming for Context-aware Defence against Advanced Persistent Threats](https://mlsanity.com/paper/2306.07685/) (2023)

## Abstract (arXiv)

Federated learning (FL) provides an emerging approach for collaboratively training semantic encoder/decoder models of semantic communication systems, without private user data leaving the devices. Most existing studies on trustworthy FL aim to eliminate data poisoning threats that are produced by malicious clients, but in many cases, eliminating model poisoning attacks brought by fake servers is also an important objective. In this paper, a certificateless authentication-based trustworthy federated learning (CATFL) framework is proposed, which mutually authenticates the identity of clients and server. In CATFL, each client verifies the server's signature information before accepting the delivered global model to ensure that the global model is not delivered by false servers. On the contrary, the server also verifies the server's signature information before accepting the delivered model updates to ensure that they are submitted by authorized clients. Compared to PKI-based methods, the CATFL can avoid too high certificate management overheads. Meanwhile, the anonymity of clients shields data poisoning attacks, while real-name registration may suffer from user-specific privacy leakage risks. Therefore, a pseudonym generation strategy is also presented in CATFL to achieve a trade-off between identity traceability and user anonymity, which is essential to conditionally prevent from user-specific privacy leakage. Theoretical security analysis and evaluation results validate the superiority of CATFL.