# NeuraNidus — Full Site Content
> Complete text of neuranidus.com for LLM ingestion.
> Last updated: 2026-08-05.

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# Nidus Synchro — Federated Learning Orchestration for the Edge

Train models across distributed devices without moving data. Open-core. Rust. Apache 2.0. Shipping Q1 2027.

## Already Powering

Applications running on Nidus Synchro today — proof the platform delivers.

### Nidus Nexus (Q3 2026)
Dynamics 365 BC ↔ SAP Ariba procurement connector. Browser-local semantic mapping with ONNX Runtime Web and WASM fallback. AL extension, AppSource-ready packaging, governed FL with tamper-evident audit trails. Available on Microsoft AppSource. Proves Nidus Synchro delivers for enterprise procurement at scale.

## How It Works — Three Planes. One Platform. Zero Data Movement.

### Node Plane — Device-Aware Training, Where the Data Lives
- Bare-metal Rust agent with no_std support for RTOS targets
- TPM/TAL hardware attestation — every node proves its integrity
- Local training with differential privacy and secure aggregation primitives
- Pluggable transport: BLE, MQTT, gRPC — adapts to connectivity context

### Adapter Plane — Framework-Agnostic. Bring Your Own Orchestrator.
- Pluggable FrameworkAdapter trait — swap Flower, TFF, FATE without changing the device layer
- Green-field custom backends through a single Rust trait contract
- Wire contracts in protobuf/flatbuffer — published, versioned, stable
- Flower Bridge eligibility for teams already invested in the Flower ecosystem

### Control Plane — Fleet-Wide Intelligence. RL-Powered. Observable.
- Fleet scheduler with topology-aware round planning across heterogeneous devices
- Reinforcement-learning policy engine for adaptive aggregation strategies
- MCP server for external tooling, dashboards, and compliance monitoring
- Structured telemetry at every decision point — latency, model drift, attestation status

## Deployment Profiles

From bare-metal microcontrollers to enterprise workstations — Nidus Synchro scales across four deployment tiers.

### Nidus-Micro — Bare-metal · <1 MB · no_std Rust
Bare-metal device agent for microcontrollers and RTOS targets. TPM/TAL attestation, TinyML inference, and secure aggregation primitives — all in under 1 MB.

### Nidus-Pro — Linux SBCs · Jetson · WebNN
Full-featured agent for Linux edge gateways, SBCs, and Jetson platforms. gRPC transport, differential privacy, and WebNN-accelerated local training.

### Nidus-Titan — Hardened Gateway · Discrete TPM
Defense-grade hardened gateway with discrete TPM, tamper-evident audit, and industrial/defense assurance profiles. For regulated environments that require hardware root of trust.

### Nidus-Local — Workstation · 128–256 GB · Local LLM
Enterprise workstation deployment with large unified memory for local LLM workloads. Run federated fine-tuning on your own hardware with full data locality.

## Use Cases — Built for the Real World

Nidus Synchro is domain-agnostic. These are some of the domains we're proving it in.

### Healthcare
Privacy-preserving diagnostics trained across hospital networks.

### Industrial IoT
Predictive maintenance on distributed sensor fleets.

### Supply Chain & Procurement
Semantic mapping between buyer templates and supplier catalogs. Federated learning preserves data locality while training shared representations across procurement workflows with governed audit trails.

### Financial Services
Fraud detection models trained across distributed branches without exposing transaction data. Differential privacy guarantees and hardware attestation ensure regulatory compliance across jurisdictions.

### Public Sector
Privacy-preserving analytics across government agencies. Train models on sensitive citizen data without centralization, with tamper-evident audit trails and role-based access control.

### Energy & Utilities
Distributed model training across smart grid sensors and substations. Federated learning on edge hardware optimizes load forecasting and anomaly detection without streaming sensitive grid data.

## Research

### Nidus Synchro: A Federated Edge Orchestrator for Privacy-Preserving Model Training
White Paper · 2026. Coming Q1 2027.
We present Nidus Synchro, an open-core federated learning orchestration platform built in Rust that spans three planes: Node, Adapter, and Control. This whitepaper describes the architecture, multi-agent RL-based fleet scheduling, and hardware-attested privacy guarantees.

## About Nidus Synchro

Nidus Synchro is an open-core federated learning orchestration platform. We build the substrate that lets organizations train machine learning models across distributed devices — from cloud servers to bare-metal microcontrollers — without centralizing sensitive data.

Every component earns its place through clear value to data processing, privacy, connectivity, or operability. The architecture is built on six engineering principles that ensure the platform stays lean, auditable, and trustworthy at every layer.

Nidus Synchro is Apache 2.0 licensed. We operate as NeuraNidus UG, based in Germany.

## Engineering Principles

1. **Functional Necessity** — Every component must justify its existence through utility. No ornament.
2. **Modular Boundaries** — Standardized, interchangeable modules with strict interface contracts.
3. **Platform Honesty** — Respect the real constraints of the target environment. No leaky abstractions.
4. **End-to-End Coherence** — Every feature is part of a full operational path from ingestion to outcome.
5. **Error Integrity** — Failures must be explicit, bounded, and observable. No silent degradation.
6. **Standardized Delivery** — A structure is trusted because it bears load under test, not because it looks sound.

## Company

**Founder & CEO:** Anton Kornilevsky

**Contact:**
- Email: contact@neuranidus.com
- Phone: +49 (176) 63074072
- Address: NeuraNidus UG, Wildstr. 26, 47057 Duisburg, Germany

**Links:**
- GitHub: https://github.com/NNidusLabs
- Homepage: https://neuranidus.com/
- Nidus Synchro: https://nidus-synchro.com/