  Privacy has raised considerable concerns recently, especially with the advent
of information explosion and numerous data mining techniques to explore the
information inside large volumes of data. In this context, a new distributed
learning paradigm termed federated learning becomes prominent recently to
tackle the privacy issues in distributed learning, where only learning models
will be transmitted from the distributed nodes to servers without revealing
users' own data and hence protecting the privacy of users.
  In this paper, we propose a horizontal federated XGBoost algorithm to solve
the federated anomaly detection problem, where the anomaly detection aims to
identify abnormalities from extremely unbalanced datasets and can be considered
as a special classification problem. Our proposed federated XGBoost algorithm
incorporates data aggregation and sparse federated update processes to balance
the tradeoff between privacy and learning performance. In particular, we
introduce the virtual data sample by aggregating a group of users' data
together at a single distributed node. We compute parameters based on these
virtual data samples in the local nodes and aggregate the learning model in the
central server. In the learning model upgrading process, we focus more on the
wrongly classified data before in the virtual sample and hence to generate
sparse learning model parameters. By carefully controlling the size of these
groups of samples, we can achieve a tradeoff between privacy and learning
performance. Our experimental results show the effectiveness of our proposed
scheme by comparing with existing state-of-the-arts.

Machine Learning;Cryptography and Security;Machine Learning