Your data never leaves. Your model keeps getting smarter.
One-Line Pitch
Distributed inference network where AMD-powered edge devices collectively fine-tune a shared foundation model using federated learning and differential privacy.
TinyLlama-1.1B-Chat (federation standard) · scales to Llama-3-8B on GPU cohorts
Primary Market
Healthcare · Finance · Legal (enterprises with strict data privacy laws)
Revenue Potential
$50B enterprise AI market
2. Problem & Opportunity
The Core Problem
Enterprises sitting on mountains of sensitive data cannot leverage modern LLMs because:
Cloud APIs leak data — Sending patient records or financial documents to OpenAI/Gemini APIs violates HIPAA, GDPR, SOC-2, and internal compliance policies.
On-prem GPUs are cost-prohibitive — A single A100 cluster costs $500K+; most mid-sized enterprises can't afford it.
Existing federated learning frameworks (Flower, FedML) are CUDA-first, poorly optimized for AMD hardware, and have no production-ready privacy stack.
No unified solution exists that combines: edge inference + privacy guarantees + heterogeneous device support + enterprise-grade compliance tooling.
The Opportunity
DATA GRAVITY
┌─────────────────────────────────────┐
│ Hospitals │ Law Firms │ Banks │
│ (HIPAA) │ (Attorney- │ (PCI │
│ │ Client │ DSS) │
└─────────────┴─────────────┴─────────┘
↓ Data can't move to cloud ↓
┌─────────────────────────────────────┐
│ FusionNet fills this gap │
│ AI comes to data, not vice versa │
└─────────────────────────────────────┘
Market signals:
Global federated learning market projected to reach $210M by 2028 (22% CAGR)
AMD's total addressable edge AI market: $50B+
Post-ChatGPT, 67% of enterprises cite data privacy as #1 blocker to AI adoption
4.3 AFLoRA (Adaptive Federated LoRA) on 4-bit Quantized Models
Original weight matrix: W ∈ R^(d×k) [frozen, 4-bit quantized]
AFLoRA decomposition: ΔW = A × Λ × B
Where:
A ∈ R^(d×r) = Global shared matrix (participates in federation)
Λ ∈ R^(r) = Local trainable diagonal importance matrix
B ∈ R^(r×k) = Local trainable matrix (remains on device)
Memory savings vs full fine-tuning:
Llama 3-8B full FT: ~32GB VRAM (BF16)
4-bit + AFLoRA (r=8): ~5GB VRAM ← feasible on Steam Deck
Personalization Guarantee: Since B and Λ never leave the device, the model inherently personalizes to local data distribution while still benefiting from the globally aggregated A matrix. Base64 encoding is used to efficiently serialize A matrices for transmission.
4.4 Heterogeneous Device Handling
Device Type
VRAM
LoRA Rank
Batch Size
Contribution Weight
MI300X (Cloud)
192GB
64
32
5.0×
Radeon RX 7900 XTX
24GB
16
8
2.0×
Ryzen AI Laptop
16GB
8
4
1.0×
Steam Deck
16GB (shared)
4
2
0.5×
CPU-Only PC
CPU RAM
2
1
0.1×
Adaptive pruning: Devices with < 8GB available VRAM receive a pruned model (40% sparsity) to participate without OOM errors.
Model Distribution Strategy:
The Base Model: Every node holds the exact same foundation model (e.g., Llama 3-8B). To ensure it fits on smaller hardware, it is heavily compressed (4-bit quantized).
LoRA Adapters: The devices do not train the massive base model. They only train a small "plugin" weight called a LoRA adapter. The size (Rank) of this adapter scales with the hardware—a cloud server trains a massive Rank 64 adapter, while a weak CPU-only laptop trains a tiny Rank 2 adapter.
Central Aggregation: The central server does not run a "bigger" model. It simply collects the LoRA adapters from all edge devices, mathematically averages them together (FedAvg), and broadcasts the smarter, combined adapter back to the network.
4.5 Zero-Knowledge Proof for Update Verification
Purpose: Prove that a device's gradient update satisfies the clipping norm constraint without revealing the actual gradients.
Prover (device) proves: ||ΔW||₂ ≤ C
Without revealing: ΔW itself
ZKP circuit: Groth16 / PLONK over BN254 curve
Verification time: ~2ms per update on coordinator
Instead of solely relying on custom kernels, FusionNet primarily integrates Opacus, a production-ready Differential Privacy library for PyTorch. However, since Opacus can sometimes struggle with dynamically quantized modules (like bitsandbytes.nn.Linear4bit), FusionNet implements an identical-interface fallback.
python
1# Setup DP-SGD with abstract PrivacyEngine2from federation.privacy import setup_privacy
34model, optimizer, dataloader, privacy_engine = setup_privacy(5 model, optimizer, dataloader, config["privacy"]6)78# During training loop9if privacy_engine:10 privacy_engine.step()# Handles gradient clipping and Gaussian noise addition11 optimizer.zero_grad()
This dual-approach ensures mathematically sound per-sample gradient clipping and noise addition, guaranteeing Differential Privacy (DP-SGD) while remaining fully compatible with ROCm backends and 4-bit quantized base models.
5.3 RCCL for Cross-Device Aggregation
python
1import torch.distributed as dist
23# Initialize RCCL process group (replaces NCCL for AMD)4dist.init_process_group(5 backend="nccl",# ROCm maps nccl → rccl automatically6 init_method="env://",7 world_size=num_devices,8 rank=device_rank
9)1011# Secure aggregation via AllReduce (MPC wraps this)12dist.all_reduce(gradient_tensor, op=dist.ReduceOp.SUM)13gradient_tensor /= num_devices
5.4 bitsandbytes for Quantized Inference
For 4-bit model loading, FusionNet uses standard transformers integrated with bitsandbytes, which now natively supports ROCm 6.0:
python
1from transformers import AutoModelForCausalLM, BitsAndBytesConfig
2import torch
34# Federation-wide model — identical across ALL client nodes.5# GPU nodes: 4-bit NF4 (~1.2 GB VRAM). CPU nodes: FP32 (~2.5 GB RAM).6FEDERATION_MODEL ="TinyLlama/TinyLlama-1.1B-Chat-v1.0"78if torch.cuda.is_available():9# GPU path — 4-bit NF4 quantization via bitsandbytes (ROCm 6.0+ supported)10 quantization_config = BitsAndBytesConfig(11 load_in_4bit=True,12 bnb_4bit_compute_dtype=torch.float16,13 bnb_4bit_quant_type="nf4",14 bnb_4bit_use_double_quant=True,15)16 model = AutoModelForCausalLM.from_pretrained(17 FEDERATION_MODEL,18 quantization_config=quantization_config,19 device_map="auto",20)21else:22# CPU path — FP32, no quantization, device_map=None23# TinyLlama-1.1B in FP32 ≈ 2.5 GB RAM; runs on any office PC.24 model = AutoModelForCausalLM.from_pretrained(25 FEDERATION_MODEL,26 torch_dtype=torch.float32,27 device_map=None,28)
6. MVP Execution Plan
Scope: 10 AMD Cloud VMs, Sentiment Analysis Task
Goal: Demonstrate that a central model improves accuracy on sentiment classification without any raw text data leaving individual VMs.
MVP Architecture
10 AMD Cloud VMs (simulate enterprise nodes)
Each VM has:
- A private text dataset partition (e.g., medical reviews, financial notes)
- Llama 3-8B loaded with 4-bit quantization
- LoRA adapter (rank=8) for local fine-tuning
Central Coordinator (1 MI300X VM):
- Receives encrypted weight deltas
- Runs FedAvg aggregation
- Broadcasts updated LoRA weights
Metrics tracked:
- Accuracy on holdout sentiment test set (per round)
- Privacy budget consumption (ε per round)
- Convergence speed vs centralized baseline
- Communication overhead (bytes per round)
MVP Timeline (2 Weeks)
Week
Days
Task
Deliverable
Week 1
1-2
Environment setup: ROCm, PyTorch, AMD Dev Cloud
Working GPU environment
3-4
Load Llama 3-8B with 4-bit quantization (MIGraphX)
Technical Report: Math proofs for DP guarantees, benchmarks vs Flower
Slides (10 slides): Problem → Solution → Demo → Business → Ask
Live Demo Link: AMD Cloud-hosted FL system running in real-time
Demo Script Outline
00:00 — Hook: "This hospital has 1M patient records. ChatGPT can't touch them."
00:30 — Problem visualization: Data gravity, compliance wall
01:00 — FusionNet architecture walkthrough (animated diagram)
02:00 — LIVE: 10 VMs fine-tuning in parallel (ROCm GPU utilization visible)
03:00 — LIVE: Accuracy graph climbing per round (model improving without data movement)
03:30 — Privacy proof: Show ε = 0.87 after 20 rounds (under budget)
04:00 — Network logs: Zero bytes of raw data transmitted
04:30 — Commercial pitch: "AMD + FusionNet = the HIPAA-compliant AI stack"
05:00 — Close: GitHub, demo link, contact
Key Differentiator Phrases for Judges
"First federated learning system natively optimized for AMD ROCm"
"Mathematically proven privacy — not just policy-level promises"
"Your Steam Deck becomes an AI trainer while you sleep"
"We don't move data to the model. We move the model to the data."
12. Team Tasks & Daily Checklist
Week 1: Core Infrastructure
Day 1 ✅
□ Provision 10 AMD Cloud VMs (MI300X or EPYC)
□ Install ROCm 6.x, PyTorch with HIP support
□ Verify GPU access: rocm-smi, torch.cuda.is_available()
Day 2 ✅
□ Download Llama 3-8B weights (HuggingFace)
□ Load with 4-bit GPTQ quantization (AutoGPTQ / bitsandbytes ROCm)
□ Benchmark inference speed on AMD GPU
Day 3 ✅
□ Implement LoRA adapter (peft library)
□ Test local fine-tuning for 100 steps on dummy data
□ Profile VRAM usage, verify fits on target devices
Day 4 ✅
□ Implement DP-SGD noise addition (Opacus or custom)
□ Write privacy accountant (track ε per step)
□ Write custom HIP noise kernel (optional optimization)
Day 5 ✅
□ Set up RCCL process group across 3 VMs
□ Test gradient AllReduce between VMs
□ Measure communication latency and bandwidth
Day 6-7 ✅
□ Build dataset partitioner (IID and non-IID splits)
□ Prepare sentiment analysis dataset (SST-2 or custom)
□ Assign partitions to VMs, verify isolation
Week 2: FL System + Demo
Day 8-9 ✅
□ Build FedAvg coordinator script
□ Run first end-to-end FL round (1 round, 3 VMs)
□ Log: accuracy, ε budget, communication bytes
Day 10-11 ✅
□ Scale to 10 VMs, run 20 rounds
□ Plot convergence curves (accuracy vs rounds)
□ Compare vs centralized baseline (upper bound)
Day 12 ✅
□ Add fault tolerance (handle VM dropout mid-round)
□ Add ZKP verification stub (or hash-based integrity check)
□ Write network packet logger (prove zero raw data transmitted)
Day 13 ✅
□ Record demo video
□ Write README and architecture doc
□ Prepare GitHub repo (clean commits, clear structure)
Day 14 ✅
□ Final review of submission
□ Submit before deadline (July 11, 2026)
□ 🎉 Ship it
📁 FusionNet Repository Structure
The local client proof-of-concept has been successfully implemented in the fusionnet-client/ directory.
During development and live testing, several issues were identified and resolved to ensure robust, serverless federated learning execution on Windows and heterogeneous hardware:
Fixed a runtime CPU/CUDA device and dtype mismatch by dynamically casting AFLoRA weights (A, B, and Lambda) to the input tensor's device and dtype inside forward().
Disabled Hugging Face datasets caching using hf_datasets.disable_caching() before running dataset mapping. This prevents a FileExistsError race condition when multiple local clients process datasets simultaneously on Windows.
Causal Language Model Target Labels Shape Mismatch (fusionnet-client/training/engine.py):
Explicitly cloned input IDs to initialize target labels (batch['labels'] = batch['input_ids'].clone()), fixing shape mismatches during local fine-tuning on classification tasks like Banking77.
🪟 Windows Quick-Start
FusionNet runs natively on Windows. All scripts have .ps1 (PowerShell) equivalents.
Step 1 — Environment Setup (run once from repo root)
Open PowerShell as Administrator (or standard user if python is globally configured) and run:
powershell
1# CPU-only (any Windows PC — works out of the box)2.\scripts\setup_env.ps1
34# NVIDIA GPU (requires driver >= 560, CUDA 12.8)5.\scripts\setup_env.ps1 -Backend cuda
67# AMD GPU (installs CPU build; for full GPU acceleration use WSL2 + setup_rocm.sh)8.\scripts\setup_env.ps1 -Backend rocm
Always activate the virtual environment (.\venv\Scripts\Activate.ps1) before executing Python scripts.
Task
Windows (PowerShell)
Linux / WSL2 (Bash)
Environment setup
.\scripts\setup_env.ps1
pip install -r requirements.txt
NVIDIA CUDA setup
.\scripts\setup_cuda.ps1
bash scripts/setup_cuda.sh
AMD ROCm setup
.\scripts\setup_rocm.ps1
bash scripts/setup_rocm.sh
Launch FL round
.\scripts\launch_fl_round.ps1
bash scripts/launch_fl_round.sh
Run single node
python main.py --client-id 0
python main.py --client-id 0
💡 Key Insights & Vision
FusionNet isn't just a federated learning tool. It's infrastructure for the AI economy where data sovereignty is a right, not a luxury.
The next billion-dollar opportunity in AI isn't building smarter models — it's building the plumbing that lets sensitive industries use them. Healthcare, legal, and finance collectively hold humanity's most valuable data and are almost entirely locked out of the LLM revolution due to legitimate privacy concerns.
FusionNet inverts the equation: instead of pulling data toward AI, it pushes AI toward data. Every AMD GPU — from a $400 Steam Deck to a $100,000 MI300X — becomes a node in a global intelligence network that learns from humanity's private knowledge while keeping that knowledge private.
This is the missing infrastructure layer for the age of private AI.
Document generated: June 2026 | FusionNet Project | AMD Developer Hackathon ACT IIDeadline: July 11, 2026
