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Locked Algorithms: The New Frontiers of Federated Learning Security
Abstract
Artificial intelligence (AI) has made complex tasks easier, and its influence is felt everywhere—from healthcare to education and beyond. One of AI’s key branches, Machine Learning (ML), is now a go-to tool for researchers and professionals, often matching or even outperforming human expertise in solving tough problems. However, privacy concerns still pose a challenge. That’s where Federated Learning (FL) steps in, offering a way to train models without users sharing their data, making the process more secure and private. In this article, we explore how FL tackles privacy and security issues, the types of threats it faces, and the protective measures used in its aggregation. We’ll also look at how homomorphic encryption safeguards data and suggest improvements to further enhance FL’s security and performance.
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