Federated Learning

Overview and Motivation

Federated Learning (FL) is a decentralized machine learning paradigm that enables multiple edge devices—referred to as clients—to collaboratively train a shared model without transferring their private data to a central location. Each client performs local training using its own dataset and communicates only model updates (such as gradients or weights) to an orchestrating server or aggregator. These updates are then aggregated to produce a new global model that is redistributed to the clients for further training. This process continues iteratively, allowing the model to learn from distributed data sources while preserving the privacy and autonomy of each client. By design, FL shifts the focus from centralized data collection to collaborative model development, introducing a new direction in scalable, privacy-preserving machine learning [1].

The motivation for Federated Learning arises from growing concerns around data privacy, security, and communication efficiency—particularly in edge computing environments where data is generated in massive volumes across geographically distributed and often resource-constrained devices. Centralized learning architectures struggle in such contexts due to limited bandwidth, high transmission costs, and strict regulatory frameworks such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). FL inherently mitigates these issues by allowing data to remain on-device, thereby minimizing the risk of data exposure and reducing reliance on constant connectivity to cloud services. Furthermore, by exchanging only lightweight model updates instead of full datasets, FL significantly decreases communication overhead, making it well-suited for real-time learning in mobile and edge networks [2].

Within the broader ecosystem of edge computing, FL represents a paradigm shift that enables distributed intelligence under conditions of partial availability, device heterogeneity, and non-identically distributed (non-IID) data. Clients in FL systems can participate asynchronously, tolerate network interruptions, and adapt their computational loads based on local capabilities. This flexibility is particularly important in edge scenarios where devices may differ in processor power, battery life, and storage. Moreover, FL supports the development of personalized and locally adapted models through techniques such as federated personalization and clustered aggregation. These properties make FL not only an effective solution for collaborative learning at the edge but also a foundational approach for building scalable, secure, and trustworthy AI systems that are aligned with emerging demands in distributed computing and privacy-preserving technologies [1][2][3].