Few-shot Federated Learning in Randomized Neural Networks via Hyperdimensional Computing

In today’s hyperconnected world, the ability to train intelligent models without centralizing data is not just an advantage, it’s a necessity. Think of privacy, the communication limits of edge networks, or devices with limited computational power. This is where a new frontier is emerging: a distributed system powered by randomized neural networks and enhanced through hyperdimensional encoding, capable of learning quickly from just a few examples while sharing only the bare minimum.

At the heart of this system are models known as Random Vector Functional Links, which can be trained extremely fast and without backpropagation. But the true innovation lies in how these models share knowledge: instead of transmitting raw data or large model weights, they exchange a compressed, hyperdimensional representation of their classifier, produced through brain-inspired operations like binding and superposition. This leads to lower network traffic, increased privacy, and faster learning.

Hyperdimensional Computing doesn’t just compress efficiently, it performs surprisingly well. Tests across more than 100 real-world datasets showed that the system maintains an average accuracy above 70%, even in challenging scenarios with uneven data distribution across nodes. In certain network topologies, such as ring structures, it exceeds 80% accuracy in complex cases, thanks to its ability to merge local knowledge into a robust global model. This solution proves reliable even under extreme conditions: from networks of 10 to 100 nodes, the model remains stable and accurate, confirming its scalability. All this is achieved with minimal communication, often just two exchanges per node.

Real-world applications? Practically endless. From healthcare to smart cities, environmental monitoring to wearables, anywhere that requires fast, distributed, privacy-respecting learning. A bold step toward lighter, more collaborative, and human-centered artificial intelligence.