Federated Learning in Practice: Building Collaborative Models While Preserving Privacy
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V3I2P109Keywords:
Federated Learning, Privacy-Preserving Machine Learning, Distributed Systems, Model Aggregation, Edge Computing, GDPR, Differential PrivacyAbstract
Federated Learning (FL) is a new method in the machine learning context that enables the training of models on decentralized devices with local data samples without transferring them. This conceptual shift offers significant benefits in terms of privacy protection, scalability, and the distribution of computing. This paper provides a detailed analysis of Federated Learning in practice, focusing on its architecture, protocols, methods for maintaining privacy, and practical applications. The literature reviews commence with the inspiration of FL, driven by increasing concerns about data protection laws such as GDPR and HIPAA. We talk about how FL can help in mitigating centralized data breaches by allowing edge devices to jointly learn a common model of prediction, but without performing the model training off-device. An extensive survey of the literature is provided, covering the current state of the art and systems that existed prior to 2022. The paper then proceeds to the methodology of FL, further describing the model aggregation techniques (FedAvg, FedProx), the design of the systems, and secure multi-party computation. We further present simulation and real-time experiment results on FL stays, such as TensorFlow Federated and PySyft. By applying a comparative study, the research establishes the potential of FL in sectors such as healthcare, finance and intelligent devices. Final observations indicate that through FL, an important privacy issue is resolved; however, open issues remain, including model drift, communication overhead, and heterogeneity. This paper serves as a primary source for scientists and others seeking to understand and apply federated learning in privacy-conscious settings
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