  Vanilla Federated learning (FL) relies on the centralized global aggregation
mechanism and assumes that all clients are honest. This makes it a challenge
for FL to alleviate the single point of failure and dishonest clients. These
impending challenges in the design philosophy of FL call for blockchain-based
federated learning (BFL) due to the benefits of coupling FL and blockchain
(e.g., democracy, incentive, and immutability). However, one problem in vanilla
BFL is that its capabilities do not follow adopters' needs in a dynamic
fashion. Besides, vanilla BFL relies on unverifiable clients' self-reported
contributions like data size because checking clients' raw data is not allowed
in FL for privacy concerns. We design and evaluate a novel BFL framework, and
resolve the identified challenges in vanilla BFL with greater flexibility and
incentive mechanism called FAIR-BFL. In contrast to existing works, FAIR-BFL
offers unprecedented flexibility via the modular design, allowing adopters to
adjust its capabilities following business demands in a dynamic fashion. Our
design accounts for BFL's ability to quantify each client's contribution to the
global learning process. Such quantification provides a rational metric for
distributing the rewards among federated clients and helps discover malicious
participants that may poison the global model.
