On the Security Privacy in Federated Learning [article]

Gorka Abad, Stjepan Picek, Víctor Julio Ramírez-Durán, Aitor Urbieta
2022 arXiv   pre-print
Recent privacy awareness initiatives such as the EU General Data Protection Regulation subdued Machine Learning (ML) to privacy and security assessments. Federated Learning (FL) grants a privacy-driven, decentralized training scheme that improves ML models' security. The industry's fast-growing adaptation and security evaluations of FL technology exposed various vulnerabilities that threaten FL's confidentiality, integrity, or availability (CIA). This work assesses the CIA of FL by reviewing
more » ... state-of-the-art (SoTA) and creating a threat model that embraces the attack's surface, adversarial actors, capabilities, and goals. We propose the first unifying taxonomy for attacks and defenses and provide promising future research directions.
arXiv:2112.05423v2 fatcat:qcovp2cz2rfgbcvx6mtx5xighe