A particle swarm optimization strategy that dynamically selects the most informative clients in federated learning matches ...

Self-supervised learning creates training targets from the structure of unlabeled data, allowing models to learn ...

A unified framework combining federated learning, differential privacy and explainable AI tackles drift, fairness and privacy ...

The global healthcare industry has entered a critical transformative era, where advancing the "AI + Healthcare" innovation ...

Google moves federated learning into TEEs, giving Gboard externally verifiable central differential privacy and faster server-side training.

Technology companies have been “running real-time, unregulated product testing on Australians” for too long, communications minister says. By Laura Chung Reporting from Sydney, Australia The ...

ABSTRACT: Background: Federated Learning has emerged as a distributed machine learning paradigm that enables entities to collaboratively train artificial intelligence models without directly sharing ...

ABSTRACT: Background: Federated Learning has emerged as a distributed machine learning paradigm that enables entities to collaboratively train artificial intelligence models without directly sharing ...

Abstract: Federated Learning (FL) has emerged as a promising framework for privacy-preserving distributed learning by enabling model training across multiple agents (clients) without exchanging raw ...

Machine learning use cases now span every major industry — from fraud detection and demand forecasting to computer vision diagnostics and predictive maintenance — giving data teams a proven playbook ...

This paper proposes FEDFTRL—the first algorithm in federated multi-armed bandits to simultaneously achieve near-optimal individual regret bounds for both stochastic and adversarial environments. The ...