Federated learning offers a new foundation for AI — one where privacy, transparency and innovation can move together.

Federated Learning in Sensitive and Regulated Domains FL can operate within government, defense, health care and utility networks, enabling private data to remain local while still contributing to ...

Federated learning makes it possible for agency employees to collaborate on advanced artificial intelligence models without compromising data control or operational security. The process serves as a ...

Federated learning (FL) allows training machine learning models on distributed data without compromising privacy. However, FL is vulnerable to model-poisoning attacks where malicious clients tamper ...

Explore how blockchain technology and federated learning are reshaping AI development with decentralized, privacy-focused governance, enabling large-scale collaboration without compromising data ...

In addition, federated learning addresses challenges posed by varying data sizes and data quality, which previously limited the effectiveness of per-advertiser model-tuning.

This paper proposes a novel framework that integrates federated learning with edge AI to overcome existing limitations and pave the way for the next generation of decentralized AI systems. The key ...