  Federated learning (FL) is a distributed machine learning (ML) technique that
enables collaborative training in which devices perform learning using a local
dataset while preserving their privacy. This technique ensures privacy,
communication efficiency, and resource conservation. Despite these advantages,
FL still suffers from several challenges related to reliability (i.e.,
unreliable participating devices in training), tractability (i.e., a large
number of trained models), and anonymity. To address these issues, we propose a
secure and trustworthy blockchain framework (SRB-FL) tailored to FL, which uses
blockchain features to enable collaborative model training in a fully
distributed and trustworthy manner. In particular, we design a secure FL based
on the blockchain sharding that ensures data reliability, scalability, and
trustworthiness. In addition, we introduce an incentive mechanism to improve
the reliability of FL devices using subjective multi-weight logic. The results
show that our proposed SRB-FL framework is efficient and scalable, making it a
promising and suitable solution for federated learning.
