Safeguarding Privacy Through Federated Machine Learning Techniques
Abstract
The chapter thoroughly addresses ML privacy threats like risks, attacks, and leaks. It explores the methods of differential privacy, homomorphic encryption, and SMPC. Federated learning is detailed, covering concepts, benefits, techniques (averaging, aggregation). Advancements include transfer learning, differential privacy, edge device use. Real cases show privacy's value in healthcare, finance, IoT. The conclusion touches on trends, regulations, privacy-utility balance. The chapter aims to overview privacy-preserving and federated ML, stressing their role in data security with insights for researchers, practitioners, policymakers for privacy-conscious ML. Valuable insights are provided for researchers, practitioners, and policymakers aiming for a privacy-conscious future in machine learning.
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