## 2 Literature Review  
Federated learning is truly a decentralized setting of model teaching with data remaining on local machines and the model updates only being shared with the central server. This, among others, was considered by [PERSON] et al. [4], and the term \"federated learning\" was coined to denote collaborative training without centralizing the sensitive data, hence preserving the privacy of the users. FL stands as a big umbrella that aims to balance the trade-off between privacy risks and large-scale data collaboration seen in centralized ML paradigms where all data sit aggregated in a common location. Some challenges unique to FL abound, including efficient communication, heterogeneity of devices, and ensuring convergence over non-IID data residing on decentralized nodes. Federated Averaging (FedAvg) [4], which averages model updates instead of providing raw data, is one of the optimal communication protocols that researchers have developed to overcome these problems. It is commonly used because it is easy to use, efficient, and suitable for a wide range of clients. A federated unlearning technique called FedEraser is intended to eliminate the impact of data points from a trained model in a targeted manner. To ensure that the surviving model no longer remembers details about the deleted instances, it works by rebuilding the training procedure without the target data. This unlearning technique comes to use specifically when data from machine learning models must be erased so that data laws like the GDPR may be complied. One of the major weaknesses of FedEraser is its high computational cost for each unlearning request, including further rounds of gradient computations and updates; however, it shows great promise in privacy preservation against due obstacles [6]. Unlearning teaches a machine to forget data points, with the consequence that a given training data point may no longer be considered for a decision to train a model. Unlearning in centralized systems can be attained by simply retraining the model with the remaining data after removing the repugnant data points. However, such an approach becomes inefficient and demanding in terms of computation in the setting of federated systems where data is distributed across devices. Federated unlearning came into the picture to counter the problem of unlearning in a decentralized environment. Thus, removing the effect of data without requiring full access to all the data nodes is the primary goal of federated unlearning, which sits well with regulations such as GDPR and CCPA. A unified federated unlearning workflow is presented in Figure 4, serving as the basis for a novel taxonomy of existing FU techniques. It defines the timeline for learning, unlearning, and verification. When the FU system receives an unlearning request, it can either allow the target client to exit the system immediately, referred to as \"Passive unlearning\", or the target client can choose to stay and participate in the unlearning process, referred to as \"Active unlearning\". Note that some unlearned clients may simultaneously initiate the unlearning request and transmit information to the server, while others may not engage in the unlearning process but remain solely for verification. The current methods tend to lose the contextual significance of data, which might result in the loss of valuable information, and are unable to ensure the least amount of overall unlearned data impact while maintaining the integrity of a model. Old techniques for federated unlearning included a certain form of local updates based on gradients that were adjusted to lessen the particular data residues to cloud a particular model's parameters. For example, [PERSON] et al. [12] proposed an approximate unlearning method, where deformation of the data point was borrowed to simulate \"forgetting\" some of the contributions.