Overview:  Model distillation is a machine learning technique that transfers knowledge from a larger, more complex model to a ...

Federated learning (FL) is a promising privacy-preserving paradigm, yet real-world applications face the challenge of model heterogeneity. Most personalized FL (PFL) methods require uniform ...

As the use of Unmanned Aerial Vehicles (UAVs) expands across various fields, there is growing interest in leveraging Federated Learning (FL) to enhance the efficiency of UAV networks. However, ...

A new mini-review maps what happens when multimodal sensing, federated privacy and continual adaptation must work ...

Forbes contributors publish independent expert analyses and insights. There’s a new wrinkle in the saga of Chinese company DeepSeek’s recent announcement of a super-capable R1 model that combines high ...

Federated learning is an emerging paradigm in which multiple participants collaboratively train machine-learning models without exchanging raw data. Instead, each participant computes local model ...

DeepSeek’s R1 release has generated heated discussions on the topic of model distillation and how companies may protect against unauthorized distillation. Model distillation has broad IP implications ...

In late January, DeepSeek released a new open-source AI model that was touted as performing comparably to proprietary frontier models at a fraction of the cost. Just ...

Model distillation is one of the technology trends that has reached a level of maturity identified in Gartner’s 2025 Hype Cycle for artificial intelligence (AI) as “the slope of enlightenment”.