Creating Privacy Preserving AI with Substra
This Hugging Face blog post covers Substra, a federated learning framework developed by Owkin for privacy-preserving AI. The post describes how Substra enables collaborative model training across institutions without sharing raw data, targeting healthcare and biomedical use cases. It represents a practical deployment pattern for federated learning in sensitive data environments.
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Federated Learning using Hugging Face and Flower
This Hugging Face blog post describes how to combine the Hugging Face ecosystem with the Flower federated learning framework to train models across distributed, privacy-preserving data silos. It provides a practical walkthrough of integrating Transformers and Datasets libraries with Flower's federated training loop. The post targets practitioners looking to apply federated learning to NLP and other ML tasks without centralizing sensitive data.
Hugging Face and JFrog Partner to Improve AI Model Security Transparency
Hugging Face and JFrog have announced a partnership aimed at improving security transparency for AI models hosted on the Hugging Face platform. The collaboration likely involves integrating JFrog's software supply chain security capabilities with Hugging Face's model repository infrastructure. This addresses growing concerns about malicious or vulnerable models being distributed through open model hubs.
Running Privacy-Preserving Inferences on Hugging Face Endpoints
Hugging Face has published a blog post describing the integration of Fully Homomorphic Encryption (FHE) with its Inference Endpoints service, enabling privacy-preserving ML inference where data remains encrypted throughout computation. The approach allows clients to send encrypted inputs to a hosted model without the server ever seeing plaintext data. This represents a practical deployment of FHE-based ML, a technique that has historically been too slow for production use but is gaining traction with recent optimizations.
Hugging Face Teams Up with Protect AI: Enhancing Model Security for the ML Community
Hugging Face has announced a partnership with Protect AI to improve security for machine learning models hosted on the platform. The collaboration aims to address vulnerabilities in model files and supply chain risks that affect the broader ML community. Specific details about the technical implementation and scope of the security enhancements are not provided in the available content.
Semi-supervised knowledge transfer for deep learning from private training data
OpenAI published research on semi-supervised knowledge transfer techniques for training deep learning models on private data, an early contribution to privacy-preserving machine learning. The work addresses how to leverage private training data without exposing sensitive information, using knowledge distillation-style approaches. This is a 2016 archival post surfaced from OpenAI's blog.
IntraShuffler: Privacy-Preserving Framework for Heterogeneous DP Federated Learning
This paper identifies a novel Privacy Inference Attack against heterogeneous differential privacy federated learning (HDP-FL) systems, where an honest-but-curious server exploits epsilon-aware aggregation and gradient denoising to infer client data distributions and link updates across rounds. To counter this, the authors propose IntraShuffler, a middleware framework that groups clients into privacy-compatible buckets and performs parameter-level shuffling within buckets, preserving epsilon-aware aggregation while disrupting persistent gradient structure. Experiments on four datasets show IntraShuffler reduces gradient recoverability by over 60% and drops surrogate inference accuracy from 0.78 to 0.33 with minimal utility loss.
Deep Learning over the Internet: Training Language Models Collaboratively
This Hugging Face blog post describes a framework for training large language models collaboratively across volunteer compute contributed over the internet. The approach addresses the challenge of enabling distributed participants with heterogeneous hardware to jointly train models without centralized infrastructure. It represents an early exploration of decentralized training as an alternative to large-scale private compute clusters.
Hugging Face Launches Inference for PRO Subscribers
Hugging Face introduced a dedicated inference tier for PRO subscribers, providing access to powerful models via API without rate limits typical of free tiers. The offering targets developers and researchers who need reliable, higher-throughput access to hosted models. This represents a monetization and infrastructure expansion move by Hugging Face to serve professional users.