Which technique is known for ensuring data privacy through federated learning?

Federated learning is a technique that allows machine learning models to be trained across multiple decentralized devices or servers holding local data samples, without exchanging them. This approach is particularly significant for ensuring data privacy, as it enables models to learn from data without exposing the raw data itself.

Nvidia FLARE (Federated Learning Application Runtime Environment) is specifically designed to facilitate federated learning by providing a framework that supports secure and efficient training of models in a distributed environment. It emphasizes privacy by keeping the training data on local devices and only sharing model updates or gradients, thereby minimizing the risk of personal data exposure. This means that sensitive information stays on the user's device, aligning with privacy regulations and protecting individual data.

Other techniques mentioned do not directly pertain to the principle of federated learning and the specific emphasis on data privacy in the same way. Hotfix deployment refers to quick patches in software systems, holistic model compression focuses on optimizing model size without particular regard for privacy, and MoE-FT (Mixture of Experts - Fine Tuning) deals with model efficiency rather than data privacy aspects. Thus, Nvidia FLARE stands out as the key technique relevant to ensuring data privacy through federated learning.