Federated Learning for Privacy-Preserving Machine Learning

Machine learning systems increasingly rely on large datasets collected from distributed users and devices, but traditional centralized approaches require transferring raw data to central servers, raising serious concerns about privacy, security, and regulatory compliance. Federated Learning (FL) addresses these issues by enabling collaborative model training without sharing raw data. In this approach, each client device trains a model locally and transmits only model updates to a central server for aggregation. This paper presents a focused study of federated learning with an emphasis on privacy-preserving mechanisms and security threats. It explores techniques such as differential privacy, secure aggregation, and homomorphic encryption to safeguard sensitive information. Additionally, it examines potential attacks, including model poisoning and inference attacks, that may compromise system integrity. The performance of federated models is evaluated using accuracy metrics and confusion matrices. The paper concludes by discussing open challenges, such as communication efficiency and robustness, and highlights future research directions.