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.
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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.
Training a Language Model with Hugging Face Transformers Using TensorFlow and TPUs
This Hugging Face blog post provides a technical walkthrough for training a language model using TensorFlow and Google TPUs via the Transformers library. It covers the practical setup, data pipeline, and training configuration required to leverage TPU hardware with the TF ecosystem. The post serves as a tutorial bridging Hugging Face tooling with TPU-based infrastructure.
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.
The Partnership: Amazon SageMaker and Hugging Face
Hugging Face and Amazon announced a partnership integrating Hugging Face models and tools natively into Amazon SageMaker. This collaboration enables developers to train and deploy Hugging Face Transformers models directly within SageMaker's managed ML infrastructure. The partnership represents an early major cloud-provider integration for Hugging Face, expanding enterprise access to open-source NLP models.
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.
Towards Encrypted Large Language Models with FHE
This Hugging Face blog post explores applying Fully Homomorphic Encryption (FHE) to Large Language Models, enabling inference on encrypted data without exposing plaintext inputs to the server. The approach aims to address privacy concerns in cloud-based LLM deployments by allowing computations to occur directly on ciphertext. The post likely covers the technical challenges of adapting transformer architectures to FHE constraints and presents early feasibility results.
Hugging Face and FriendliAI Partner to Supercharge Model Deployment on the Hub
Hugging Face and FriendliAI have announced a partnership to integrate FriendliAI's inference infrastructure directly into the Hugging Face Hub. The collaboration aims to simplify and accelerate model deployment for developers accessing models through the Hub. This expands the ecosystem of inference providers available on Hugging Face's platform.
Scaling AI-based Data Processing with Hugging Face + Dask
Hugging Face published a blog post describing how to scale AI-based data processing pipelines by combining Hugging Face datasets and models with Dask, a parallel computing framework. The post covers patterns for distributed inference and large-scale dataset preprocessing. This is a practical integration guide targeting ML engineers who need to process data at scale beyond single-machine limits.