## 2 Background Material--Introduction to FL
### FL Basics  
FL pioneers a unique approach: instead of transferring data to a central point for analysis and model building [2; 3], the training itself takes place where the data reside; see Figure 1.  
FL allows multiple users in a network to utilize their local data to contribute to an integrated, global model. This process, while ensuring data privacy, also uses the diverse and expansive nature of the data residing on these devices, leading to more robust and generalizable models.  
The FL process consists of three core parts.  
* Learning Algorithm and Training: The server trains a model by repeating several steps, like designing the learning algorithm, selecting clients, distributing the model, updating the client model, and server-side aggregation and model updates.  
Figure 1: Federated learning architecture.  
* Privacy Protection Mechanism: FL can protect users' data privacy by employing data encryption training, ensuring that the model does not reveal the original data. Additionally, encrypting the data transmission process ensures that only intermediary results without extra information are relayed during the training process.
* Incentive Mechanism: Since FL relies on participation, it is essential to offer adequate incentives to participants. The incentive mechanism strives to equitably share the benefits of FL, motivating users to participate consistently and deterring malicious actors from dominating the process. A more analytical outline of the FL process can be provided as follows [4].
* Initialization: On a server, a global model is created.
* Model Distribution: This global model is dispatched to the various participating nodes or devices.
* Local Training: Each node dedicates itself to training this model using its reservoir of local data.
* Model Update Sharing: After this training, the nodes send back model updates to the central server.
* Aggregation: The central entity aggregates these updates, often employing techniques like Federated Averaging, as proposed by [5].
* Iteration: The previous three steps (distribution, training, and aggregation) are reiterated until the model achieves an acceptable level of performance.  
Despite FL's design, issues persist, such as ensuring the twin objectives of secure and efficient communication, navigating the heterogeneity of systems, handling skewed and non-IID data, and combating malicious threats. In [6], the authors specifically underscore the importance of data security and the need for efficient communication channels that prioritize the transfer of only the most pivotal model updates, minimizing data breach risks. Several distinct concerns associated with the more conventional centralized learning approach are addressed seamlessly by FL, which can be categorized into the following three classes.  
* Regulatory Hurdles: Globally, there has been a surge in regulations, such as GDPR in Europe, CCPA in California, and PDPB in India. These laws present strict rules regarding the movement and mixture of sensitive data, especially when the data protection standards vary across regions. FL addresses these regulatory complexities by minimizing data movement.
* User Privacy: Users are increasingly concerned about the privacy of their data. Applications that involve inputting sensitive information like passwords or credit card details raise expectations that such details remain on the device, shielded from external servers. FL addresses this expectation, reinforcing users' trust.
* Data Volume and Bandwidth: A growing number of devices, especially those like surveillance cameras, generate vast data volumes. Transferring this vast amount of data centrally is not only a logistical challenge but is often not economically viable. By enabling on-site training, FL substantially reduces the bandwidth demands, making it a more scalable option.