federated-xgboost
mc2
Our great sponsors
| federated-xgboost | mc2 | |
|---|---|---|
| 1 | 8 | |
| 64 | 291 | |
| - | 0.7% | |
| 1.2 | 0.7 | |
| about 1 year ago | about 1 year ago | |
| C++ | C++ | |
| - | Apache License 2.0 |
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federated-xgboost
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Federated XGBoost
Have you seen this github repo? https://github.com/mc2-project/federated-xgboost
mc2
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Intel deprecates SGX on Core series processors
Analytics and ML on confidential data are some interesting server side use cases. See the MC2 open source project, for example: https://github.com/mc2-project/mc2
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How to Run Spark SQL on Encrypted Data
Check out more blog posts on how to securely process data with MC² Project. We would love your contributions ✋ and support ⭐! Please check out the Github repo to see how you can contribute. No contribution is too small.
- Show HN: MC² – Secure collaborative analytics and ML
- MC2: Secure Collaborative Analytics and ML
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Announcing MC²: Securely perform analytics and machine learning on confidential data
MC2 is a platform for running secure analytics on data that stays encrypted even when in use. By doing so, the project also enables secure collaboration among multiple organizations, where individual data owners can use our platform to jointly analyze their collective data without revealing it to one another. To learn more and to see the individual projects’ documentation, visit our landing page.
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Secure collaborative analytics and ML on encrypted data using MC²
Whoops. My bad: https://github.com/mc2-project/mc2
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[P] Secure collaborative analytics and ML on encrypted data using MC²
Our team @ UC Berkeley has been working on a platform for secure analytics and machine learning called MC2 -- and today we are excited to announce the initial release v0.1 of the platform! With MC2, you can take encrypted data and run various analytics and machine learning workloads at near processor speeds, while keeping the data confidential. MC2 also enables secure collaboration -- mutually distrustful data owners can jointly analyze / train models on their data, but without revealing their data to each other. Github: https://github.com/mc2-project/mc2
What are some alternatives?
flower - Flower: A Friendly Federated Learning Framework
delphi - A Cryptographic Inference Service for Neural Networks
FedML - FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on any GPU cloud or on-premise cluster. Built on this library, FEDML Nexus AI (https://fedml.ai) is your generative AI platform at scale.
opaque-sql - An encrypted data analytics platform
secure-xgboost - Secure collaborative training and inference for XGBoost.
breaching - Breaching privacy in federated learning scenarios for vision and text
cerebro - Cerebro: A platform for Secure Coopetitive Learning
AIJack - Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing