Federated Learning: Collaborative Intelligence in Decentralized Environments

Abstract

The abstract focuses on Federated Learning, a groundbreaking paradigm in machine learning, facilitating collaborative intelligence within decentralized environments. This paper navigates through the foundational concepts and pivotal aspects of Federated Learning, elucidating its role in training models across distributed devices without centrally aggregating raw data. It delves into the architectural framework, highlighting its potential to preserve data privacy, minimize communication costs, and enable scalable machine learning. Moreover, the abstract addresses the challenges inherent in this approach, including communication constraints, model heterogeneity, and security concerns. The discussion culminates in an exploration of future directions, envisioning expanded applications and refined protocols to harness the full potential of Federated Learning in diverse domains.