Abstract
This invention provides a next‑generation federated‑learning engine for edge devices requiring privacy‑preserving model training without transmitting raw data. The system integrates multi‑modal sensor fusion, temporal‑artifact removal, adaptive gradient compression, differential‑privacy noise shaping, and resonance‑stabilized aggregation to improve accuracy, reduce bandwidth, and enhance privacy. The core architecture uses stabilized multi‑domain tensors and artifact‑cleaned gradients to ensure reliable convergence across heterogeneous devices. The key physics principle is that multi‑modal fusion and resonance‑aligned aggregation reduce drift and noise during distributed learning. This disclosure includes full architecture, BOM, build path, and test protocol. This work is intended as open‑hardware prior art to ensure global public access.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Caldwell, Michael Victor Mr., "CALDWELL MULTI‑MODAL FEDERATED‑LEARNING ENGINE WITH DIFFERENTIAL PRIVACY & RESONANCE‑STABILIZED AGGREGATION (FL‑NEXUS‑2.0)", Technical Disclosure Commons, (September 07, 2026)
https://www.tdcommons.org/dpubs_series/11622