Google TechTalks
POPri: Private Federated Learning using Preference-Optimized Synthetic Data
Meta research introduces POPri, a novel approach using Reinforcement Learning to fine-tune LLMs for generating high-quality synthetic data under strict privacy constraints in federated learning, significantly outperforming prior methods.
Chasing the Constants and its Implications in Differential Privacy
Discover how refining mathematical constants in differential privacy algorithms significantly reduces error in continual data streams, impacting applications from disease tracking to private federated learning.
Differentially Private Synthetic Data without Training
Microsoft Research introduces 'Private Evolution,' a novel framework that generates differentially private synthetic data using only inference APIs, bypassing the high costs and limitations of traditional DP fine-tuning.
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