Abstract: Federated learning enables participants to collaboratively train a global model through distributed training without sharing raw data. However, this distributed training is vulnerable to ...

Abstract: Recent years have witnessed a huge demand for artificial intelligence and machine learning applications in wireless edge networks to assist individuals with real-time services. Federated ...

The energy sector is becoming a highly connected cyber-physical ecosystem in which distributed energy resources, electric ...

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

Atharv Kolhar, a staff test automation engineer at Figure AI, says the robotics industry needs a testing philosophy that ...

As patients are divided into ever more narrowly defined subgroups, the number of individuals available for research shrinks dramatically. While this approach improves personalization, it also creates ...

APPFL, Advanced Privacy-Preserving Federated Learning, is an open-source and highly extensible software framework that allows research communities to implement, test ...

In an era where data breaches make headlines weekly and privacy regulations tighten globally, artificial intelligence faces a fundamental challenge: how to learn from data without compromising privacy ...

A collaborative approach to training AI models can yield better results, but it requires finding partners with data that complements your own. José Parra-Moyano, Karl Schmedders, and Maximilian Werner ...

As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a ...

Each year, cyberattacks become more frequent and data breaches become more expensive. Whether companies seek to protect their AI system during development or use their algorithm to improve their ...