  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 the 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.
