## 2 Background  
Federated learning (FL), as shown in Figure 1, is a machine learning technique that significantly contributes to preserving privacy and protecting data during the learning process. In federated learning, two types of learning models exist: local and global learning models. The global learning model resides in the cloud server and distributes parameters to the local learning models. These local fog node models receive the parameters and conduct model learning locally. Subsequently, the updated parameters are sent back to the global model. These updated parameters iteratively proceed forward and backward until reaching the target with the minimum error, as explained in Algorithm 1[14; 15]. Integrating of FL and blockchain in credit card services ensure preserved privacy, data protection, decentralized storage, secure payment networks, and automated tasks [16; 17]. Figure 1: Federated learning. Federated learning is a recent ML technique that has gained popularity for its ability to train ML classifier models on local devices or servers without sharing private data with cloud servers, thus ensuring privacy preservation and data protection. Various applications leverage federated learning, including healthcare, financial services, smartphones and IoT devices, autonomous vehicles, telecommunications, manufacturing, retail, energy and utilities, agriculture, and government and public services. [PERSON] et al. proposed a distributed ML technique called federated learning (FL) in [PERSON] et al. (2014). It presented a practical method for the federated learning of deep networks based on iterative model averaging, and it conducted a comprehensive empirical evaluation across five different models and four datasets. [PERSON] et al. proposed \"FedSI\", a novel federated continual learning method that adapts the synaptic intelligence method to the federated learning scenario. This approach aims to enhance performance on non-IID data by incorporating knowledge from other local models. They developed CFedSI, a communication-efficient federated continual learning method that reduces communication overheads by employing the bidirectional compression and error compensation (BCEC) algorithm. This BCEC algorithm works on compressing transmitted data, both uplink and downlink, while ensuring training divergence through error compensation [PERSON] et al. (2018). [PERSON] et al. introduced a \"DILoCC\" framework to oversee various devices, including wearable devices, sensors, and applications. This architecture utilized a Distributed Incremental Learning (DIL) approach, enabling collaboration among the sensing devices. Additionally, it enhances system efficiency by mitigating the repercussions of \"Catastrophic Forgetting\". Hence, the paper elucidated how IoMT and wearable devices enhance healthcare through predictive ICT systems. Additionally, \"DILoC\" oversees wearables using a Distributed Incremental Learning approach [PERSON] et al. (2018). In their paper, [PERSON] et al. [PERSON] et al. (2018) explored the utilization of Federated Learning (FL) technology in medical applications. They provided an overview of current research trends and outcomes aimed at designing reliable and scalable FL models. [PERSON] et al. introduced a taxonomy of existing incentive mechanisms for federated learning, accompanied by comparison and contrast of various approaches to incentive mechanisms [PERSON] et al. (2018). Many research papers have proposed surveys and conducted in-depth discussions about federated learning technology, as indicated in Table 1.  
Blockchain is a new technology with decentralized storage, tamper resistance, and traceability. It operates as a distributed ledger consisting of multiple nodes. Therefore, each node stores all data independently of a central authority. A blockchain consists of a series of blocks that hold specific information connected to form a chain structure in the chronological order in which they are generated. It utilizes cryptographic knowledge to ensure its immutability and unforgeability.