Security and Privacy Issues of Federated Learning

Autor:	Hasan, Jahid
Rok vydání:	2023
Předmět:	Computer Science - Cryptography and Security Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	Federated Learning (FL) has emerged as a promising approach to address data privacy and confidentiality concerns by allowing multiple participants to construct a shared model without centralizing sensitive data. However, this decentralized paradigm introduces new security challenges, necessitating a comprehensive identification and classification of potential risks to ensure FL's security guarantees. This paper presents a comprehensive taxonomy of security and privacy challenges in Federated Learning (FL) across various machine learning models, including large language models. We specifically categorize attacks performed by the aggregator and participants, focusing on poisoning attacks, backdoor attacks, membership inference attacks, generative adversarial network (GAN) based attacks, and differential privacy attacks. Additionally, we propose new directions for future research, seeking innovative solutions to fortify FL systems against emerging security risks and uphold sensitive data confidentiality in distributed learning environments. Comment: 6 pages, 2 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2307.12181 Zobrazit plný text záznamu View this record from Arxiv