Incentive-Driven Federated Learning and Associated Security Challenges:
A Systematic Review
Abstract
In response to various privacy risks, researchers and practitioners have
been exploring different paradigms that can leverage the increased
computational capabilities of consumer devices to train machine (ML)
learning models in a distributed fashion without requiring the uploading
of the training data from individual devices to central facilities. For
this purpose, federated learning (FL) was proposed as a technique that
can learn a global machine model at a central master node by the
aggregation of models trained locally using private data. However,
organizations may be reluctant to train models locally and to share
these local ML models due to required computational resources for model
training at their end and due to privacy risks that may result from
adversaries inverting these models to infer information about the
private training data. Incentive mechanisms have been proposed to
motivate end users to participate in collaborative training of ML models
(using their local data) in return for certain rewards. However, the
design of an optimal incentive mechanism for FL is challenging due to
its distributed nature and the fact that the central server has no
access to clients’ hyperparameters information and the amount/quality
data used for training, which makes the task of determining the reward
based on the contribution of individual clients in FL environment
difficult. Even though several incentive mechanisms have been proposed
for FL, a thorough up-to-date systematic review is missing and this
paper fills this gap. According to the best of our knowledge, this paper
is the first systematic review that comprehensively enlists the design
principles required for implementing these incentive mechanisms and then
categorizes various incentive mechanisms according to their design
principles. In addition, we also provide a comprehensive overview of
security challenges associated with incentive-driven FL. Finally, we
highlight the limitations and pitfalls of these incentive schemes and
elaborate upon open-research issues that required further research
attention.