The amalgamation of federated learning and blockchain technology is mainly due to the privacy and security concerns in multiple data sharing in unreliable environments. This blockchain-based federated learning helps limit various complications such as communication cost, single point of failure, malicious data, false clients, poison attacks, and lack of incentives and privacy leakage with the decentralized blockchain technology framework. Blockchain-enabled federated learning is utilized for diverse real-world applications by overcoming all shortcomings. The key applications are the Internet of Vehicles (IoV), Internet of Things (IoT), Healthcare Applications, Edge Computing, Smart Cities, Mobile Devices, Recommendation Systems, Financial fields, Data Mining, Smart Transportation, Vehicular Networks, 5G and 6G networks.
Some of the open research problems in secured blockchain-based federated learning are malicious miners, the dark side of immutable storage, exploitation of smart contracts, vulnerabilities in blockchain frameworks, synchronization issue, and blockchain forking issue. A few potential future directions of Blockchain-enabled federated learning that need more attention in many real-time applications are improving decentralized federated learning performances, promoting participation, and advancing security and privacy. Various literature surveys and reviews have been conducted under the Blockchain-enabled federated learning and its specific applications. All the surveys and reviews present overviews, open issues, challenges, solutions, application scenarios, used cases, architectures, and future research scopes of Blockchain-enabled federated learning. Such published survey and review papers are highlighted below;