quinkgl 0.2.7

A decentralized gossip learning framework for P2P edge intelligence

QuinkGL: Decentralized Federated Learning Framework

QuinkGL is a decentralized, peer-to-peer (P2P) machine learning framework that enables training models across distributed devices without a central server. Using gossip-based protocols, nodes exchange model updates directly with each other, achieving convergence through decentralized aggregation.

Overview

Traditional federated learning relies on a central parameter server to aggregate model updates from all clients. QuinkGL eliminates this single point of failure and bottleneck by using gossip protocols—each node communicates with a random subset of peers each round, and updates propagate through the network organically.

Key Features

Feature	Description
Decentralized	No central server required
Gossip Learning	Random walk and gossip-based aggregation
P2P Networking	IPv8 with NAT traversal (UDP hole punching)
Tunnel Fallback	Automatic relay server when direct P2P fails
Framework Agnostic	PyTorch, TensorFlow, or custom models
Multiple Strategies	Topology and aggregation strategies
Byzantine Resilient	Built-in robust aggregation options
Monitoring	Optional MCP integration for AI assistants

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Installation

pip install quinkgl

For development:

git clone https://github.com/aliseyhann/QuinkGL-Gossip-Learning-Framework.git
cd QuinkGL-Gossip-Learning-Framework
pip install -e .

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Quick Start

import asyncio
import torch.nn as nn
from quinkgl import GossipNode, PyTorchModel

# 1. Define your model
class SimpleNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(784, 128)
        self.fc2 = nn.Linear(128, 10)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = x.view(x.size(0), -1)
        return self.fc2(self.relu(self.fc1(x)))

# 2. Wrap the model
model = PyTorchModel(SimpleNet(), device="cpu")

# 3. Create and run the node
async def main():
    node = GossipNode(
        node_id="alice",
        domain="mnist",
        model=model,
        port=7000
    )

    await node.start()
    await node.run_continuous(training_data)
    await node.shutdown()

asyncio.run(main())

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Architecture

┌─────────────────────────────────────────────────────────────┐
│                       GossipNode                            │
│  (Production-ready node with P2P networking + fallback)     │
├─────────────────────────────────────────────────────────────┤
│  ┌─────────────┐  ┌──────────────┐  ┌───────────────────┐  │
│  │ PyTorchModel│  │RandomTopology│  │      FedAvg       │  │
│  │ TensorFlow  │  │ CyclonTopology│ FedProx │ FedAvgM  │  │
│  └─────────────┘  └──────────────┘  TrimmedMean │ Krum  │  │
├─────────────────────────────────────────────────────────────┤
│  ┌───────────────────────────────────────────────────────┐ │
│  │              ModelAggregator                           │ │
│  │        (Train → Gossip → Aggregate Cycle)             │ │
│  └───────────────────────────────────────────────────────┘ │
├─────────────────────────────────────────────────────────────┤
│  ┌───────────────────────────────────────────────────────┐ │
│  │            IPv8 Network Layer + Tunnel Fallback       │ │
│  │         (P2P, NAT Traversal, UDP, Relay)              │ │
│  └───────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────┘
         │
         │ (Optional MCP Monitoring)
         ▼
┌─────────────────────────────────────────────────────────────┐
│  MetricsCollector ──► MCPServer ──► AI Assistant (Claude)   │
└─────────────────────────────────────────────────────────────┘

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Project Structure

QuinkGL/
├── src/quinkgl/
│   ├── core/
│   │   └── learning_node.py      # Framework node (network-agnostic)
│   ├── models/
│   │   ├── base.py               # ModelWrapper, TrainingConfig
│   │   ├── pytorch.py            # PyTorch implementation
│   │   └── tensorflow.py         # TensorFlow implementation
│   ├── topology/
│   │   ├── base.py               # TopologyStrategy base
│   │   ├── random.py             # RandomTopology
│   │   └── cyclon.py             # CyclonTopology
│   ├── aggregation/
│   │   ├── base.py               # AggregationStrategy base
│   │   ├── fedavg.py             # FedAvg implementation
│   │   ├── fedprox.py            # FedProx implementation
│   │   ├── fedavgm.py            # FedAvgM implementation
│   │   ├── trimmed_mean.py       # TrimmedMean implementation
│   │   └── krum.py               # Krum and MultiKrum implementations
│   ├── gossip/
│   │   ├── protocol.py           # Message types, validation
│   │   └── aggregator.py         # Training→gossip→aggregation cycle
│   ├── network/
│   │   ├── gossip_node.py        # GossipNode (IPv8 + tunnel)
│   │   ├── ipv8_manager.py       # IPv8 wrapper
│   │   └── gossip_community.py   # IPv8 community
│   ├── storage/
│   │   └── model_store.py        # Model checkpointing
│   ├── serialization/
│   │   └── weights.py            # Model weight serialization helpers
│   ├── data/
│   │   └── datasets.py           # Data loading utilities
│   └── mcp/
│       ├── metrics.py            # Metrics collection
│       └── server.py             # MCP server
└── tests/

Package Responsibilities

• core: public node abstraction without transport concerns
• gossip: round orchestration and protocol primitives
• network: IPv8 transport and wire delivery
• aggregation: model merge strategies
• topology: peer selection and partial-view management
• serialization: model weight serialization helpers

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Public API Overview

Core Classes

Class	Description
LearningNode	Framework node without networking
GossipNode	Production node with P2P + tunnel fallback
GLNode	Backward compatibility alias for LearningNode

Use LearningNode when you control the transport layer yourself. Use GossipNode when you want the built-in IPv8 networking stack.

Model Wrappers

Class	Description
PyTorchModel	Wrapper for PyTorch nn.Module
TensorFlowModel	Wrapper for TensorFlow/Keras models
ModelWrapper	Base class for custom wrappers
TrainingConfig	Training configuration (epochs, batch_size, lr, grad_clip)
TrainingResult	Training result with metrics

Topology Strategies

Class	Description
RandomTopology	Random peer selection each round
CyclonTopology	Shuffling for network exploration
PeerInfo	Peer information dataclass
SelectionContext	Context for peer selection

Aggregation Strategies

Strategy	Type	Description
FedAvg	Standard	Weighted averaging by sample count
FedProx	Advanced	Proximal term for heterogeneous data
FedAvgM	Advanced	Server momentum for stability
TrimmedMean	Byzantine	Remove extreme values
Krum	Byzantine	Select most central update
MultiKrum	Byzantine	Average most central updates

Data Utilities

The quinkgl.data helpers are not part of the documented public API in this repository snapshot. The package root may expose placeholders for DatasetLoader, FederatedDataSplitter, and DatasetInfo, but they are not guaranteed to resolve to usable imports in the current build.

Monitoring (Optional)

Class	Description
MetricsCollector	Collect metrics from nodes
MCPServer	MCP server for AI assistant integration

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Documentation

Document	Description
QUINKGL_FRAMEWORK.md	Complete user guide with all features and examples

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MCP Integration

QuinkGL includes built-in support for MCP (Model Context Protocol), enabling AI assistants like Claude to monitor your experiments:

from quinkgl import MetricsCollector, MCPServer

collector = MetricsCollector()
collector.attach(gossip_node)

server = MCPServer(collector)
await server.start()

Available MCP Tools:

• get_nodes – List all monitored nodes
• get_training_progress – Training status per node
• get_accuracy_history – Accuracy over rounds
• get_model_exchanges – Model send/receive records
• analyze_convergence – Network convergence analysis

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Requirements

• Python 3.9+
• PyTorch 1.9+ (optional, for PyTorchModel)
• TensorFlow 2.x (optional, for TensorFlowModel)
• IPv8 2.0+ (for P2P networking)

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License

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Copyright 2026 Ali Seyhan, Baki Turhan

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Contributing

Contributions are welcome! Please read our contributing guidelines and submit pull requests to the main repository.