<ul><li>Deep learning has shown incredible potential across various tasks, but accessing data stored on personal devices poses privacy challenges.</li><li>Federated learning (FL) has emerged as a privacy-preserving technology that enables collaborative training of machine learning models without sending raw data to a central server.</li><li>This survey paper provides a literature review of privacy attacks and defense methods in FL, identifies limitations, and discusses successful industry applications.</li><li>The paper also explores the efficacy of a hybrid federated-continual learning paradigm for robust web phishing detection, achieving high accuracy and outperforming traditional approaches.</li></ul>

Updates on Federated Learning part1(AI 2024)

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