Privacy-Preserving in Blockchain-based Federated Learning Systems
Sameera K. M. Serena Nicolazzo Marco Arazzi Antonino Nocera Rafidha Rehiman K. A. Vinod P Mauro Conti
Federated Learning (FL) has recently arisen as a revolutionary approach to
collaborative training Machine Learning models. According to this novel
framework, multiple participants train a global model collaboratively,
coordinating with a central aggregator without sharing their local data. As FL
gains popularity in diverse domains, security, and privacy concerns arise due
to the distributed nature of this solution. Therefore, integrating this
strategy with Blockchain technology has been consolidated as a preferred choice
to ensure the privacy and security of participants.
  This paper explores the research efforts carried out by the scientific
community to define privacy solutions in scenarios adopting Blockchain-Enabled
FL. It comprehensively summarizes the background related to FL and Blockchain,
evaluates existing architectures for their integration, and the primary attacks
and possible countermeasures to guarantee privacy in this setting. Finally, it
reviews the main application scenarios where Blockchain-Enabled FL approaches
have been proficiently applied. This survey can help academia and industry
practitioners understand which theories and techniques exist to improve the
performance of FL through Blockchain to preserve privacy and which are the main
challenges and future directions in this novel and still under-explored
context. We believe this work provides a novel contribution respect to the
previous surveys and is a valuable tool to explore the current landscape,
understand perspectives, and pave the way for advancements or improvements in
this amalgamation of Blockchain and Federated Learning.