Work

Federated reservoir intelligence for distributed temporal and neuromorphic learning

Year: 2026

Type: article

Abstract: Federated learning lets many devices train a shared model without pooling their private data, yet applying it to time-varying and event-driven signals is expensive: deep recurrent and spiking networks must exchange large numbers of parameters in every communication round. Here we show that combining federated learning with reservoir computing reduces this bottleneck. Each device runs a fixed, untrained recurrent network and shares only a compact linear output layer or its summary statistics. With shared reservoir features and full participation, sharing statistics reconstructs the centralized solution in one round; the iterative alternative converges quickly under intermittent participation; personalization and exponentially-discounted temporal updates also follow in closed form. On brain–computer interface and event-camera benchmarks, the approach matches locally trained models or deep baselines in accuracy-matched settings while reducing communication severalfold, and a compressed variant lowers communication by more than two orders of magnitude where an accuracy trade-off is acceptable. It requires no backpropagation, suiting low-power neuromorphic hardware.

Source: Communications Engineering

DOI: 10.1038/s44172-026-00755-9

Author: Liang Dong

Institutions: Baylor University, The University of Texas Southwestern Medical Center

Language: English

Topic: Neural Networks and Reservoir Computing

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,190

Funders: National Science Foundation, National Institutes of Health, National Cancer Institute