  Edge computing and distributed machine learning have advanced to a level that
can revolutionize a particular organization. Distributed devices such as the
Internet of Things (IoT) often produce a large amount of data, eventually
resulting in big data that can be vital in uncovering hidden patterns, and
other insights in numerous fields such as healthcare, banking, and policing.
Data related to areas such as healthcare and banking can contain potentially
sensitive data that can become public if they are not appropriately sanitized.
Federated learning (FedML) is a recently developed distributed machine learning
(DML) approach that tries to preserve privacy by bringing the learning of an ML
model to data owners'. However, literature shows different attack methods such
as membership inference that exploit the vulnerabilities of ML models as well
as the coordinating servers to retrieve private data. Hence, FedML needs
additional measures to guarantee data privacy. Furthermore, big data often
requires more resources than available in a standard computer. This paper
addresses these issues by proposing a distributed perturbation algorithm named
as DISTPAB, for privacy preservation of horizontally partitioned data. DISTPAB
alleviates computational bottlenecks by distributing the task of privacy
preservation utilizing the asymmetry of resources of a distributed environment,
which can have resource-constrained devices as well as high-performance
computers. Experiments show that DISTPAB provides high accuracy, high
efficiency, high scalability, and high attack resistance. Further experiments
on privacy-preserving FedML show that DISTPAB is an excellent solution to stop
privacy leaks in DML while preserving high data utility.
