Machine Learning is fast changing mobile app development, particularly in the Android ecosystem, which powers more than 70% of smartphones worldwide as of 2024. With its capability to learn from data, ...

Our goal is to provide a federated learning framework that is both secure ... Sign up for a FEDn Studio account and take the Quickstart tutorial to get started with FEDn. Use of our multi-tenant, ...

The tutorial concludes by examining emerging research directions, including adaptive privacy budgets and federated learning approaches, establishing a foundation for privacy-preserving smart home ...

Abstract: Federated learning (FL) is a distributed machine learning technique enabling multiple clients to jointly train a global model while preserving the privacy of their non-IID (non-independent ...

The authors do not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and have disclosed no relevant affiliations beyond ...

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

LLMs face challenges in continual learning due to the limitations of parametric knowledge retention, leading to the widespread adoption of RAG as a solution. RAG enables models to access new ...

Learning useful features from large amounts of unlabeled images is important, and models like DINO and DINOv2 are designed for this. These models work well for tasks like image classification and ...