Crypto Intersects AI - Round Table Discussion
Explore the intersection of cryptography and AI in this roundtable discussion from the Glowing Hot Topics in Cryptography workshop, featuring expert insights on emerging trends and challenges.
Privacy-Preserving Machine Learning Workshop 2023 - Contributed Talks Session II
Explore cutting-edge research in privacy-preserving machine learning through expert presentations at the PPML 2023 workshop, covering innovative techniques and applications in the field.
Privacy-Preserving Machine Learning - Invited and Contributed Talks
Explore privacy-preserving machine learning with expert Mariana Raykova and contributed talks from PPML 2023, covering cutting-edge research and advancements in secure AI and data protection.
Privacy-Preserving Machine Learning Workshop - Invited and Contributed Talks
Explore cutting-edge research in privacy-preserving machine learning with expert talks and contributions at PPML 2023, covering innovative techniques and advancements in the field.
FedLess - Secure and Scalable Serverless Federated Learning
Explore serverless federated learning with FedLess, addressing scalability and security challenges in collaborative model training across distributed clients using cloud platforms.
Fundamentals of Privacy-Preserving Federated Learning
Explore privacy-preserving techniques in federated learning, focusing on data protection and collaborative model training across distributed datasets.
OpenFL: A Federated Learning Framework for Secure Collaborative Model Training
Explore OpenFL, a Python framework for federated learning that enables collaborative model training without sharing sensitive data. Learn its features, applications, and security benefits.
Federated AI in Future Digital Banking
Explore how WeBank leverages federated AI and open-source machine learning to revolutionize digital banking, addressing data privacy and regulatory challenges while enhancing customer experience.
Federated Learning in 5G C-V2X
Explore federated learning in 5G C-V2X, covering network requirements, autonomous vehicle architecture, challenges, and practical applications through a demo and Q&A session.
ACUMOS AI Project - Machine Learning Platform Overview
Explore ACUMOS AI, an open-source platform for ML workflow, trusted AI, and federated learning. Learn about cloud migration, edge deployment, and practical use cases in this comprehensive overview.
Securing ML Workloads with Kubeflow and MLOps - Pwned By Statistics
Learn to secure machine learning workloads using Kubeflow and MLOps. Explore techniques like model extraction, hidden data attacks, and leakage detection to protect your ML pipelines and models.
OpenFL - Building Better AI Models with Private Data
Explore federated learning for collaborative AI model development without sharing sensitive data. Learn about OpenFL's applications in healthcare and space research.
How to Adopt Emerging Federated Learning Technologies
Explore Flame, a flexible system for deploying distributed federated learning applications, enabling customization and easy adoption of new architectures with reduced development effort.
SHERPA: Explainable Robust Algorithms for Privacy-Preserved Federated Learning in Future Networks
Explore robust algorithms for privacy-preserved federated learning in future networks, focusing on explainable and secure data collaboration techniques.
LOKI: Large-Scale Data Reconstruction Attack Against Federated Learning Through Model Manipulation - 2024
Explore a large-scale data reconstruction attack against federated learning, focusing on model manipulation techniques and their implications for privacy and security.
Practical Defenses Against Adversarial Machine Learning
Explore real-world attacks on ML systems, from recommendation engines to email filters, and learn practical defense strategies against adversarial machine learning threats.
Protect Privacy in a Data-Driven World - Privacy-Preserving Machine Learning
Explore privacy-preserving machine learning techniques to harness AI's power while maintaining data confidentiality, with insights on federated learning, trusted execution environments, and homomorphic encryption.
Federated Learning - Private Distributed ML
Explore federated learning for private, distributed machine learning across devices or entities, addressing data sharing constraints while enabling collaborative model training.
Oort - Efficient Federated Learning via Guided Participant Selection
Efficient federated learning system that improves model training and testing by intelligently selecting participants based on data utility and device capabilities, enhancing accuracy and performance.
BatchCrypt - Efficient Homomorphic Encryption for Cross-Silo Federated Learning
Efficient homomorphic encryption system for cross-silo federated learning, reducing encryption and communication overhead while maintaining privacy and accuracy in collaborative machine learning.
Comprehensive Privacy Analysis of Deep Learning
Análisis exhaustivo de la privacidad en el aprendizaje profundo, destacando ataques de inferencia de caja blanca y vulnerabilidades en modelos centralizados y federados.