  Since there are multiple parties in collaborative learning, malicious parties
might manipulate the learning process for their own purposes through backdoor
attacks. However, most of existing works only consider the federated learning
scenario where data are partitioned by samples. The feature-partitioned
learning can be another important scenario since in many real world
applications, features are often distributed across different parties. Attacks
and defenses in such scenario are especially challenging when the attackers
have no labels and the defenders are not able to access the data and model
parameters of other participants. In this paper, we show that even parties with
no access to labels can successfully inject backdoor attacks, achieving high
accuracy on both main and backdoor tasks. Next, we introduce several defense
techniques, demonstrating that the backdoor can be successfully blocked by a
combination of these techniques without hurting main task accuracy. To the best
of our knowledge, this is the first systematical study to deal with backdoor
attacks in the feature-partitioned collaborative learning framework.
