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Federated Learning (FL) has gained widespread popularity in recent years dueto the fast booming of advanced machine learning and artificial intelligencealong with emerging security and privacy threats. FL enables efficient modelgeneration from local data storage of the edge devices without revealing thesensitive data to any entities. While this paradigm partly mitigates theprivacy issues of users' sensitive data, the performance of the FL process canbe threatened and reached a bottleneck due to the growing cyber threats andprivacy violation techniques. To expedite the proliferation of FL process, theintegration of blockchain for FL environments has drawn prolific attention fromthe people of academia and industry. Blockchain has the potential to preventsecurity and privacy threats with its decentralization, immutability,consensus, and transparency characteristic. However, if the blockchainmechanism requires costly computational resources, then theresource-constrained FL clients cannot be involved in the training. Consideringthat, this survey focuses on reviewing the challenges, solutions, and futuredirections for the successful deployment of blockchain in resource-constrainedFL environments. We comprehensively review variant blockchain mechanisms thatare suitable for FL process and discuss their trade-offs for a limited resourcebudget. Further, we extensively analyze the cyber threats that could beobserved in a resource-constrained FL environment, and how blockchain can playa key role to block those cyber attacks. To this end, we highlight somepotential solutions towards the coupling of blockchain and federated learningthat can offer high levels of reliability, data privacy, and distributedcomputing performance.

A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing