Editing Federated Learning (section)

= 5.1 Overview and Motivation =

'''Federated Learning (FL)''' is a decentralized approach to machine learning where many edge devices—called clients—work together to train a shared model ''without'' sending their private data to a central server. Each client trains the model locally using its own data and then sends only the model updates (like gradients or weights) to a central server, often called an '''aggregator'''. The server combines these updates to create a new global model, which is then sent back to the clients for the next round of training. This cycle repeats, allowing the model to improve while keeping the raw data on the devices. FL shifts the focus from central data collection to collaborative training, supporting privacy and scalability in machine learning systems.<sup>[1]</sup>

The main reason for using FL is to address concerns about data privacy, security, and communication efficiency—especially in edge computing, where huge amounts of data are created across many different, often limited, devices. Centralized learning struggles here due to limited bandwidth, high data transfer costs, and strict privacy regulations like the '''General Data Protection Regulation (GDPR)''' and the '''Health Insurance Portability and Accountability Act (HIPAA)'''. FL helps solve these problems by keeping data on the device, reducing the risk of leaks and the need for constant cloud connectivity. It also lowers communication costs by sharing small model updates instead of full datasets, making it ideal for real-time learning in mobile and edge networks.<sup>[2]</sup>

In edge computing, FL is a major shift in how we do machine learning. It supports distributed intelligence even when devices have limited resources, unreliable connections, or very different types of data ('''non-IID'''). Clients can join training sessions at different times, work around network delays, and adjust based on their hardware limitations. This makes FL a flexible option for edge environments with varying battery levels, processing power, and storage. FL can also support personalized models using techniques like '''federated personalization''' or '''clustered aggregation'''. Overall, it provides a strong foundation for building AI systems that are scalable, private, and better suited for the challenges of modern distributed computing.<sup>[1]</sup><sup>[2]</sup><sup>[3]</sup>