  Social bot detection is of paramount importance to the resilience and
security of online social platforms. The state-of-the-art detection models are
siloed and have largely overlooked a variety of data characteristics from
multiple cross-lingual platforms. Meanwhile, the heterogeneity of data
distribution and model architecture makes it intricate to devise an efficient
cross-platform and cross-model detection framework. In this paper, we propose
FedACK, a new federated adversarial contrastive knowledge distillation
framework for social bot detection. We devise a GAN-based federated knowledge
distillation mechanism for efficiently transferring knowledge of data
distribution among clients. In particular, a global generator is used to
extract the knowledge of global data distribution and distill it into each
client's local model. We leverage local discriminator to enable customized
model design and use local generator for data enhancement with hard-to-decide
samples. Local training is conducted as multi-stage adversarial and contrastive
learning to enable consistent feature spaces among clients and to constrain the
optimization direction of local models, reducing the divergences between local
and global models. Experiments demonstrate that FedACK outperforms the
state-of-the-art approaches in terms of accuracy, communication efficiency, and
feature space consistency.
