Learn how federated learning helps meet global data sovereignty laws like GDPR and HIPAA by enabling secure, privacy-preserving data collaboration.| Duality Technologies
The machine learning community has consistently found that while modern machine learning (ML) models are powerful, they often need to be fine-tuned on domain-specific data to maximize performance. This can be problematic or even impossible, as informative data is often privacy-sensitive. Differential privacy (DP) allows us to train ML models while rigorously guaranteeing that the learned model respects the privacy of its training data, by injecting noise into the training process.| research.google
こんにちは，ふたばとです． 今回は最近開発している自作の連合学習フレームワーク『FutabatedLearning』を紹介をしてみようと思います． 最低限人に見せられるよう整えたので LICENSE を MIT にしてリポジトリを公開しました． github.com 連合学習とは，機械学習におけるプライバシーの保護に重点を置いた学習手法です． 一般的な機械学習を１つの中央のサーバにデータを...| アルゴリズム弱太郎