Data Scientist Ke Zhang’s Research Explores Homomorphic Encryption for Privacy-Preserving Marketing

A privacy-preserving marketing framework applies homomorphic encryption to perform machine learning on encrypted ...
BizTech

Federated Machine Learning Gives Enterprises a Competitive AI Advantage

Historically, machine learning models have been trained by consolidating data from multiple sources into a centralized cloud server or data center and then training the model based on the combined ...
HealthTech

Federated Machine Learning Gives Healthcare Organizations a Competitive AI Advantage

Historically, machine learning models have been trained by consolidating data from multiple sources into a centralized cloud server or data center and then training the model based on the combined ...
labs.sogeti

FEDERATED LEARNING: THE FUTURE OF COLLABORATIVE AND CONFIDENTIAL AI IN MEDICAL IMAGING

The field of medicine and medical imaging (X-rays, MRIs, CT scans, etc.) is rich in data, creating fertile ground for Artificial Intelligence (AI). Machine learning models, particularly deep neural ...
IEEE

Privacy Preserving Machine Learning With Federated Personalized Learning in Artificially Generated Environment

Abstract: The widespread adoption of Privacy Preserving Machine Learning (PPML) with Federated Personalized Learning (FPL) has been driven by significant advances in ...
techxplore

Lancelot federated learning system combines encryption and robust aggregation to resist poisoning attacks

Federated learning is a machine learning technique that allows several individuals, dubbed "clients," to collaboratively train a model, without sharing raw training data with each other. This "shared ...
AFCEA

Decentralized Defense: How Federated Learning Strengthens U.S. AI

The Great Power Competition is no longer confined to traditional warfare. It plays out in data, algorithms and artificial intelligence (AI). As adversaries weaponize misinformation and cyber attacks ...
Scientific Research Publishing

Yang, Q., Liu, Y., Chen, T. and Tong, Y. (2019) Federated Machine Learning: Concept and Applications. ACM Transactions on Intelligent Systems and Technology, 10, 1-19.

ABSTRACT: This paper presents a comprehensive machine learning approach for credit score classification, addressing key challenges in financial risk assessment. We propose an optimized CatBoost-based ...