• Python 3.10 or higher
• pip

For Tier 2/3 (edge devices) or basic usage:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -e .

DeepSpeed requires PyTorch to be installed first. Install in this order:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install PyTorch first (required for DeepSpeed build)
pip install torch>=2.0

# Then install the package with Tier 1 dependencies
pip install -e ".[tier1]"

Alternatively, install everything in one step (PyTorch will be installed first automatically):

pip install torch>=2.0
pip install -e ".[tier1]"

For development with testing and linting tools:

pip install -e ".[tier1,dev]"

This document describes the design of a federated, nested-learning framework that:

• Trains Mixture-of-Experts (MoE) models across heterogeneous compute tiers:
  • Tier 1: HPC clusters / data centers.
  • Tier 2/3: Edge devices (IoT, vehicles, mobiles).
• Uses Nested Learning concepts to enable continual learning without catastrophic forgetting.
• Ensures strict data privacy and governance:
  • Global governance in data centers (DataHub, Apache Ranger, Flyte, MLflow).
  • Local, airgapped governance on edge devices (sidecar).
• Is orchestrated with Flyte and coordinated via Flower (or similar FL framework).
• Uses Kafka for event streaming and HAProxy/VPN for secure connectivity.
• Exposes a CLI-first, modular Python framework that can later be wrapped as a service/app.

This document is intended for project planning, implementation, and infra design.

1. Federated Training Across Multiple Clusters

   • Training must occur in a federated fashion across multiple clusters (Tier 1) and devices (Tier 2/3).
   • No raw data leaves any cluster or device by default.

2. Heterogeneous Node Types

   • Nodes can be:
     • Servers / HPC clusters (Tier 1).
     • IoT devices, vehicles, mobile devices (Tier 2/3).
   • Mixed hardware and network conditions.

3. Tier-Specific Training Capabilities

   • Tier 1:
     • Train core foundational model.
     • Train MoE experts (feed-forward networks).
     • Can also train LoRA/QLoRA adapters if desired.
   • Tier 2/3:
     • Train only local adapters (LoRA/QLoRA-like for experts).
     • Core weights stay frozen.

4. Hierarchical Asynchronous Co-Training

   • Multiple nested loops:
     • Fast, local training at edge.
     • Slower, heavy training at Tier 1.
     • Event-driven aggregation and model versioning.
   • No strict synchronous rounds required; system must tolerate stale updates.

5. Nested Learning Principle

   • Treat global training as an outer optimization.
   • Treat per-node training (especially adapters) as nested inner optimizations.
   • Design to mitigate catastrophic forgetting.

6. Data & Privacy

   • Data should not leave its cluster or device unless explicitly allowed.
   • Common format: JSONL for LLM fine-tuning.
   • PII detection / anonymization where any text leaves local boundaries.

7. Governance & Compliance

   • Global governance: DataHub, Apache Ranger, MLflow, Flyte.
   • Local governance: Sidecar per edge node, airgapped, no central metadata leakage.

8. Model Versioning & Observability

   • Automated model versioning (MLflow).
   • Monitoring & observability for training, infra, and telemetry.

9. Scalable Eventing & Networking

   • Kafka for control and update streams.
   • HAProxy and VPN for secure connectivity from edge to central infra.

10. Developer Experience

    • Primary interface: CLI + Python library.
    • Later: optional API service / UI.

• Tier 1 – HPC / Data Center

  • Runs full MoE model: core + experts.
  • Heavy distributed training (DeepSpeed + PyTorch).
  • Full integration with global control plane:
    • Flyte (orchestration).
    • DataHub (metadata).
    • Apache Ranger (data access control).
    • MLflow (runs & model registry).
    • Observability stack (Prometheus, Grafana, logs, tracing).

• Tier 2/3 – Edge Nodes (IoT / Vehicles / Mobiles)

  • Receive frozen global model snapshot (quantized if necessary).
  • Train only expert adapters (LoRA/QLoRA).
  • Use local governance sidecar:
    • Enforce “model-delta-only” export.
    • Maintain local audit logs, never shipped centrally.

Three nested loops:

1. Inner Loop (Edge / Local Adapters)
   • Each device trains adapters on its own data.
   • Produces ΔW_experts_local (adapters-only deltas).
2. Middle Loop (Tier 1 / Core & Experts)
   • Clusters train:
     • Core W_core on global data.
     • Experts W_experts_global on large-scale datasets.
   • Integrate ΔW_experts_local from edge nodes.
3. Outer Loop (Global Control Plane)
   • Flyte orchestrates:
     • When to do core updates.
     • When to aggregate expert updates.
     • When to evaluate & promote new model versions.
   • Data Engine-like feedback to generate new tasks.

We explicitly partition model parameters:

1. Core (W_core)
   • Token & positional embeddings.
   • Self-attention weights (Q, K, V, O).
   • Normalization layers (LayerNorm, RMSNorm).
   • Optionally gating network weights (if we don’t want devices to alter routing).
2. Global Experts (W_experts_global)
   • Set of feed-forward experts: {W_E1, W_E2, ..., W_En}.
   • Hosted and trained primarily at Tier 1.
   • Sharded and parallelized using DeepSpeed MoE.
3. Adapters / LoRA Layers
   • Tier 1 Adapters (optional): Domain-specific adapters for specific clusters or datasets.
   • Tier 2/3 Adapters (mandatory): Low-rank matrices (A_i, B_i) on top of expert weights:
     • W_Ei_eff = W_Ei + A_i · B_iᵀ
     • Only (A_i, B_i) trainable on edge nodes.

• Global gating network g(x; W_gate):
  • Trained at Tier 1 on large-scale data.
• On Tier 2/3:
  • Option 1: Gating fully frozen, only expert functions adapted via adapters.
  • Option 2: Minimal gating adapters (e.g. per-expert bias) that stay local only and are not aggregated centrally.

Steps on each edge node:

1. Receive Model Snapshot
   • Pull W_core, W_experts_global, and model version v via FedServer or Kafka control events.
   • Optionally receive quantized weights (QLoRA / 4-bit for memory efficiency).
2. Initialize Local Model
   • Load frozen W_core, W_experts_global.
   • Attach local adapters (A_i, B_i) for selected experts.
3. Local Training
   • Train with local private dataset (JSONL or equivalent).
   • Objective: minimize local loss L_local, updating only adapters.
   • Use resource-aware training: mixed precision, tiny batch sizes, small learning rates.
4. Governance Filtering
   • Sidecar validates outbound payload:
     • Must be strictly ΔW_experts_local (or new adapter weights).
     • No raw data, no detailed metadata, no PII.
   • Sidecar writes local audit logs.
5. Send Update
   • ΔW_experts_local published to Kafka topic updates.experts.local via VPN + HAProxy.
6. Telemetry
   • Minimal training metrics & statuses (loss, success/failure) published to telemetry.edge topic (optionally DP-noised).

This loop is asynchronous: Node trains when resources are available; central system ingests deltas continuously.

Inputs: Global datasets, streams of ΔW_experts_local, configs/tasks.

Steps:

1. Core & Expert Training (CoreUpdate Workflow)
   • Flyte triggers a “core update” workflow.
   • Ranger authorizes training services to access cluster data.
   • DeepSpeed + PyTorch load W_core, W_experts_global and train on large-scale data.
   • Compute ΔW_core_T1, ΔW_experts_T1.
2. Integrate Edge Expert Updates
   • Aggregation worker (FedServer or dedicated process) consumes updates.experts.local.
   • Apply aggregation strategy (weight by recency, data size, reliability; downweight stale updates).
3. Global Parameter Update
   • Combine updates:

     W_core    ← W_core    + η_core * ΔW_core_T1
     W_experts ← W_experts + η_T1   * ΔW_experts_T1
                           + η_T2   * ΔW_experts_agg_from_edges
     

4. Evaluation & Nested Learning Safeguards
   • Evaluate updated model on global validation sets and regressions.
   • Apply anti-forgetting strategies: Regularization (EWC), selective freezing, or distillation.
5. Versioning & Promotion
   • Log run to MLflow; register new model version v+1.
   • Update DataHub with lineage.
   • Governance/SecOps approve or reject new version.
6. Broadcast New Version
   • FedServer / Kafka emits ModelAvailable/RoundStart for version v+1.

• Apache Ranger: Defines and enforces data access policies. Audits data access.
• DataHub: Central catalog of datasets, models, features. Maintains lineage (Dataset → Run → Model).
• MLflow: Tracks experiments, metrics, artifacts. Model registry with stages.
• Flyte: Orchestrates workflows (training, aggregation, eval). Encodes governance in pipeline definitions.
• Presidio: PII detection/anonymization for exceptional cross-silo sharing.

Sidecar responsibilities:

• Policy Enforcement: Local static config (YAML/JSON) or OPA-like rules defining allowed endpoints and payload fields.
• Outbound Filter: Validates payloads (no raw text, no identifiers).
• Local Audit Logs: Tracks local training/export events; stored on device only.
• Optional Local Presidio: Lightweight PII detection for logs.

This implements dual governance: Full-featured governance in Tier 1; Minimal, strictly enforced governance in Tier 2/3 with airgapped metadata.

• VPN (WireGuard / IPSec): Secure overlay for edge nodes to reach central cluster.
• HAProxy / Ingress: Public ingress/load-balancer for FedServer/Kafka. Terminates TLS.
• mTLS & Auth: Mutual TLS certificates/tokens binding identity (client_id) to connections.

• Edge Node: Edge Runtime → Sidecar → VPN Tunnel → VPN Gateway → HAProxy → FedServer / Kafka
• Tier 1: Internal communication within cluster (FedServer, Kafka, Flyte) behind firewall.

1. control.federation_rounds: Events: RoundStart, ModelAvailable, config updates.
2. updates.experts.local: Payload: ΔW_experts_local + minimal metadata.
3. telemetry.edge: Payload: Aggregated loss metrics, training outcome (DP/noise optional).
4. tasks.training: Payload: Structured TrainingTask objects (core_update, expert_aggregation).

• Loose coupling: Decouples training, aggregation, and orchestration.
• Replayability: Allows reconstruction of training history/debugging.
• Data Engine: Telemetry feeds analytics which drive new tasks.

Primary project: CLI-first Python package (can later host a service).

federated-nested-moe/
├── pyproject.toml            # packaging, dependencies, entrypoints
├── README.md                 # high-level docs
├── docs/                     # detailed doc for architecture, governance, etc.
├── examples/                 # sample configs, Flyte workflows, Dockerfiles
├── src/
│   └── fednested/
│       ├── cli.py            # main CLI (typer/click)
│       ├── config/           # Pydantic models and config loader
│       ├── model/            # MoE model, adapters, checkpoints, quantization
│       ├── training/         # tier1_trainer, tier2_trainer, aggregation
│       ├── federation/       # Flower-based server/client wrappers
│       ├── governance/       # ranger, datahub, local_sidecar, presidio integration
│       ├── orchestration/    # Flyte helpers, reference workflows
│       ├── observability/    # logging, metrics, tracing
│       ├── messaging/        # kafka_client, topic management, events
│       ├── networking/       # vpn tools, haproxy templates, security helpers
│       └── utils/            # generic utilities
└── tests/
    └── ...                   # unit/integration tests

• fednested.model
  • moe_model.py: Definition of MoE architecture and parameter partitioning.
  • adapters.py: LoRA/QLoRA layers and APIs.
  • checkpointing.py: Load/save state_dicts, split/merge core/experts/adapters.
• fednested.training
  • tier1_trainer.py: Orchestrates deep MoE training on HPC (DeepSpeed).
  • tier2_trainer.py: Edge training loops (adapters only).
  • aggregation.py: Logic for combining ΔW_experts.
• fednested.federation
  • server.py: Custom FedServer hooks into Kafka & aggregation.
  • client.py: Edge client runtime.
• fednested.governance
  • local_sidecar.py: Local enforcement engine.
  • global_metadata.py: DataHub + MLflow hooks.
• fednested.orchestration
  • flyte_integration.py: Tasks wrapping CLI calls.

We use Typer or Click for a clean CLI.

• fednested init-config tier1
  • Generate sample Tier 1 config (model, data, Ranger, MLflow settings).
• fednested tier1 core-update --config config/tier1.yaml
  • Run a Tier 1 core + experts training cycle.
• fednested tier1 aggregate-experts --config config/tier1.yaml
  • Run aggregator: consume updates.experts.local.
• fednested tier2 run-client --config config/tier2.yaml
  • Start edge client: join federation, train local adapters, send deltas.
• fednested messaging bootstrap-topics
  • Create required Kafka topics.
• fednested infra generate-vpn-config --profile edge
  • Generate VPN client config for edge node enrollment.

• Environment: Kubernetes or HPC cluster with GPU nodes.
• Components: Flyte, Kafka, FedServer, HAProxy, DataHub, MLflow, Ranger.
• Execution: Run as K8s Jobs using tier1_trainer.py with DeepSpeed.

• Environment: IoT devices, vehicles, mobiles (Container or native).
• Components: fednested client library, Local Sidecar, VPN client.
• Constraints: Model quantization (QLoRA), energy-aware scheduling.

1. Phase 1 – Core Framework & Tier 1 Only

   • Implement MoE model + adapters.
   • Implement Tier 1 training with DeepSpeed.
   • Integrate MLflow + DataHub + Ranger.

2. Phase 2 – Federated Edge Training + Dual Governance

   • Implement edge client + adapters-only training.
   • Implement sidecar governance.
   • Configure VPN + HAProxy + Kafka.

3. Phase 3 – Nested Learning Optimizations & Data Engine

   • Add anti-forgetting strategies.
   • Implement smarter aggregation schemes.
   • Implement telemetry consumer & Data Engine.

4. Phase 4 – Optional Control Plane Service / UI

   • FastAPI service for listing models/tasks.
   • Web UI for monitoring and governance approvals.

• Complexity: Managing Flyte, Flower, Kafka, Ranger, DataHub, etc. requires strong automation/IaC.
• Edge Resources: Aggressive quantization is likely required.
• Security: Adversarial clients could poison training; robust mTLS and sidecars are essential.
• Governance Consistency: Dual pipelines must encode consistent policies despite using different tools.