  Vertical Federated Learning (VFL) has emerged as a collaborative training
paradigm that allows participants with different features of the same group of
users to accomplish cooperative training without exposing their raw data or
model parameters. VFL has gained significant attention for its research
potential and real-world applications in recent years, but still faces
substantial challenges, such as in defending various kinds of data inference
and backdoor attacks. Moreover, most of existing VFL projects are
industry-facing and not easily used for keeping track of the current research
progress. To address this need, we present an extensible and lightweight VFL
framework VFLAIR (available at https://github.com/FLAIR-THU/VFLAIR), which
supports VFL training with a variety of models, datasets and protocols, along
with standardized modules for comprehensive evaluations of attacks and defense
strategies. We also benchmark 11 attacks and 8 defenses performance under
different communication and model partition settings and draw concrete insights
and recommendations on the choice of defense strategies for different practical
VFL deployment scenarios.
