Federated Core Training of a 27B EURO STACK LLM Using SYNNQ Pulse.
Project Summary
1. Objective
This document outlines the technical architecture and execution plan for the federated core training of a 27 billion-parameter foundational language model—the next phase of the EURO STACK LLM—using SYNNQ Pulse, a distributed orchestration infrastructure designed for privacy-compliant, scalable AI training. This 27B model forms the next baseline layer of a sovereign European LLM, trained exclusively on curated, audited, and legally compliant datasets, across a heterogeneous and decentralized network of compute nodes in Europe.
2. Training Framework Overview
The SYNNQ Pulse system facilitates the training of large-scale models in environments where:
- Data cannot be centralized (due to legal or trust constraints),
- Compute is highly heterogeneous (ranging from HPC clusters to enterprise GPUs),
- Interoperability and fault tolerance are critical.
The goal is to distribute the training process across dozens or hundreds of compute nodes using federated orchestration, while ensuring:
- Training data integrity and version control,
- Hardware-aware workload scheduling,
- Secure training result integration.
3. Dataset Preparation and Sharding
3.1 Dataset Curation
SYNNQ Pulse curates the additional 20B training dataset from high-quality sources that satisfy:
- GDPR and EU AI Act compliance,
- Verified licensing and content provenance,
- Sectoral and linguistic diversity reflecting European use cases.
3.2 Data Sharding
Once curated, the dataset is partitioned into training shards, each a discrete semantic unit (e.g., by topic, language, document type). Shards are defined by:
- Uniform token count (target ~10M tokens per shard),
- Balanced content complexity,
- Consistent domain representation.
Each shard is assigned a unique identifier and is digitally signed to ensure auditability and content traceability.
4. Training Orchestration Steps
Step 1: Shard Allocation
Using node capability profiles, SYNNQ Pulse matches data shards to participating nodes. Matching criteria include:
- Architecture compatibility (e.g., optimized for CUDA or ROCm),
- Training batch size vs. memory capacity,
- Energy efficiency and thermal constraints.
Step 2: Local Training Cycle
Each node unpacks the training shard and begins fine-tuning a shared model architecture checkpoint, using containerized training environments (Docker/Singularity) to ensure consistency.
Training duration:
- Target: 5 epochs per shard (~2–5 GPU-hours depending on node class).
- Training environment: PyTorch + DeepSpeed/FSDP optimization.
Each local model logs:
- Gradient norms,
- Loss curves,
- Token throughput,
- System diagnostics (power draw, utilization).
Step 3: Submission and Integration
Nodes send back the trained shard checkpoint and associated logs to the SYNNQ Aggregation Layer. This layer performs:
- Integrity checks (checksum, signature validation),
- Performance scoring (e.g., convergence, underfitting detection),
- Outlier filtering (detects noise or adversarial gradients),
- Optional differential privacy pass-through (configurable by context).
Step 4: Federated Aggregation
Training outputs are merged using Federated Averaging and Gradient Scaling techniques. Key steps:
- Layer-wise normalization of updates,
- Time-decay weighting for asynchronous contributors,
- Domain balancing to prevent data dominance from any sector.
A new global checkpoint is constructed and redistributed for the next training round.
5. Training Cycle Management
Training follows iterative synchronized rounds.
Each round involves:
- New shard-node mappings (avoiding training redundancy),
- Global checkpoint refinement and redistribution,
- Updated learning rate schedule and optimizer states.
Checkpoint evaluation uses:
- Cross-validation on held-out EU test set,
- Perplexity and F1 benchmarks on public NLP tasks,
- Internal SYNNQ compliance test suite (bias, toxicity, explainability).
6. Fault Tolerance and Redundancy
The system incorporates real-time monitoring via SYNNQ Pulse’s Control Plane. Key features:
- Node drop detection and fallback reassignment,
- Checkpoint failover and rollback,
- Shard redistribution upon node failure or underperformance,
- Anomaly detection in training curves.
All interactions are logged immutably for auditing and reproducibility.
7. Security and Data Handling Protocols
To ensure security and compliance:
- Data shards are encrypted at rest and in transit.
- Only training output (no raw text) is sent back to SYNNQ.
- Participating nodes sign a Federated Compute Participation Agreement, affirming data retention, isolation, and destruction terms.
- All training logs are anonymized prior to central analysis.
8. Expected Output
Upon completion of the iterative training process, SYNNQ Pulse will release:
- A 27B parameter LLM checkpoint trained entirely on audited EU data, with verified provenance.
- Evaluation benchmarks and model cards detailing compliance, use cases, and known limitations.
- Inference APIs for initial use by stakeholders (e.g., government, healthcare, legal).
This model serves as the baseline foundation for larger follow-on models (24B, 70B, and beyond), reusing the same federated orchestration layer.
9. Technical Benefits
Benefit | Description
Hardware-agnostic | Utilizes any CUDA/ROCm-compatible GPU (MI250X, A100, etc.)
Energy-efficient | Leverages idle infrastructure, reducing carbon footprint
Privacy-preserving | No raw data leaves the node
Legally compliant | Fully aligns with GDPR and EU AI Act
Transparent & auditable| Full process logging and shard traceability
10. Conclusion
The core training of a 27B parameter foundational LLM using SYNNQ Pulse proves that federated, privacy-compliant AI training is not only possible, but scalable, secure, and efficient. By intelligently matching curated data with Europe’s diverse compute infrastructure, SYNNQ Pulse lays the foundation for a truly sovereign AI capability — built by Europe, for Europe.
Navid Kiani Larijani, SYNNQ Pulse, Germany