FedPilot Framework Overview

FedPilot is an open-source federated learning framework built on Ray for scalable distributed machine learning. It enables organizations to train machine learning models collaboratively while preserving data privacy.

Repository: github.com/fedpilot

What is FedPilot?

Federated Learning (FL) is a machine learning paradigm where:

Training data remains distributed across multiple locations
Each participant trains locally on their own data
Only model updates are shared with other participants
A global model is built without centralizing data

FedPilot provides a complete platform for implementing federated learning systems, from research experiments to production deployments.

Core Features

Federated Learning Architectures

Centralized FL (Star Topology): Traditional server-client architecture
Decentralized FL (Ring/K-Connected): Peer-to-peer collaboration without central server

Supported Models

Vision: CNN, LeNet, ResNet-18/50, VGG-16, MobileNet, Vision Transformer (ViT), Swin
NLP: BERT, ALBERT
Custom Models: Full support for user-defined models

Available Datasets

Image: MNIST, Fashion-MNIST, FEMNIST, CIFAR-10/100, SVHN, STL-10, Tiny ImageNet
Text: Shakespeare, BBC News, Yahoo QA
Custom Datasets: Support for proprietary data

Optimization & Privacy

Aggregation Strategies: FedAvg (IID), FedProx (heterogeneous data)
Differential Privacy: DP-SGD implementation with privacy budgets
Model Compression: Pruning, quantization, enhanced chunking
Advanced Techniques: Gradient clipping, client sampling, secure aggregation

Architecture Overview

FedPilot follows a layered architecture:

graph TB
    subgraph UI["User Interface Layer"]
        CLI["CLI Commands"]
        Config["Configuration"]
        Monitor["Monitoring"]
    end
    
    subgraph App["Application Layer"]
        Star["Star Topology"]
        Ring["Ring Topology"]
        KConn["K-Connected"]
    end
    
    subgraph Core["Core FL Layer"]
        FedBase["FederatedBase"]
        FedNode["FederatedNode"]
        VNode["VirtualNode"]
    end
    
    subgraph Comm["Communication Layer"]
        TM["TopologyManager"]
        MQ["Message Queue"]
        GOS["Global Object Store"]
    end
    
    subgraph Train["Training & Optimization"]
        TrainLoop["Training Loop"]
        Agg["Aggregators"]
        DP["Differential Privacy"]
        Prune["Model Pruning"]
    end
    
    subgraph Ray["Ray Runtime"]
        RM["Resource Manager"]
        Dist["Distributed Execution"]
    end
    
    UI --> App
    App --> Core
    Core --> Comm
    Comm --> Train
    Train --> Ray

Key Components

FederatedBase

Central coordinator that:

Manages all federated nodes
Initializes and manages topology
Orchestrates training rounds
Handles resource allocation

FederatedNode

Individual participant that:

Loads and trains on local data
Computes model updates
Participates in aggregation
Reports metrics

VirtualNode

Lazy-initialized wrapper providing:

Resource efficiency for large deployments
Automatic conversion to FederatedNode on first use
Scalability to thousands of participants

TopologyManager

Communication coordinator handling:

Network connectivity
Message routing
Dynamic topology adaptation
Round synchronization

Communication Flow

sequenceDiagram
    participant S as Server
    participant C1 as Client 1
    participant C2 as Client 2
    participant C3 as Client 3
    
    Note over S,C3: Round N
    S->>C1: Send Global Model
    S->>C2: Send Global Model
    S->>C3: Send Global Model
    
    Note over C1,C3: Local Training
    C1->>C1: Train on local data
    C2->>C2: Train on local data
    C3->>C3: Train on local data
    
    C1->>S: Send Updates
    C2->>S: Send Updates
    C3->>S: Send Updates
    
    Note over S: Aggregate Updates
    S->>S: FedAvg/FedProx
    
    Note over S,C3: Round N+1

Data Privacy Model

FedPilot protects data privacy through:

Local Training: Data never leaves client machines
Gradient Aggregation: Only model updates are shared
Differential Privacy: Optional DP-SGD adds noise to gradients
Secure Aggregation: Encrypted gradient aggregation
Client Sampling: Random selection of participants per round

Privacy Guarantees

With Differential Privacy enabled:

Epsilon (ε): Privacy budget spent
Delta (δ): Probability of privacy breach
Example: ε=1.0, δ=1e-5 provides strong privacy

Use Cases

Research

Federated learning algorithm development
Privacy-preserving model training
Non-IID data analysis
Aggregation strategy testing

Enterprise

Collaborative model training across organizations
On-device model training
Privacy-compliant machine learning
Regulatory compliance (GDPR, HIPAA, etc.)

Mobile & Edge

Federated learning on edge devices
Mobile model training
IoT data processing
Real-time model updates

Healthcare & Finance

Patient data privacy protection
Financial institution collaboration
Regulatory requirement compliance
Sensitive data protection

Federated Learning Architectures

1. Centralized (Star Topology)

graph TB
    Server["Server<br/>Aggregator"]
    C1["Client 1"]
    C2["Client 2"]
    C3["Client 3"]
    C4["Client 4"]
    
    Server ---|Model| C1
    Server ---|Model| C2
    Server ---|Model| C3
    Server ---|Model| C4
    
    C1 ---|Updates| Server
    C2 ---|Updates| Server
    C3 ---|Updates| Server
    C4 ---|Updates| Server

Characteristics:

Central server coordinates
All-to-one communication pattern
Synchronous training rounds
Ideal for traditional FL

2. Decentralized (Ring Topology)

graph LR
    C1["Client 1"]
    C2["Client 2"]
    C3["Client 3"]
    C4["Client 4"]
    
    C1 ---|Exchange| C2
    C2 ---|Exchange| C3
    C3 ---|Exchange| C4
    C4 ---|Exchange| C1

Characteristics:

No central server
Peer-to-peer communication
Gossip-based aggregation
Resilient to failures

3. K-Connected Topology

graph TB
    C1["Client 1"]
    C2["Client 2"]
    C3["Client 3"]
    C4["Client 4"]
    
    C1 ---|Exchange| C2
    C1 ---|Exchange| C4
    C2 ---|Exchange| C3
    C3 ---|Exchange| C4

Characteristics:

Each client connected to K neighbors
Flexible communication pattern
Balanced gossip-style aggregation
Scalable mesh network

Characteristics:

Ray cluster with head and workers
Task-based distributed training
Elastic scalability
Production-ready monitoring

Aggregation Strategies

FedAvg (Federated Averaging)

Best for: IID (independent and identically distributed) data

global_model = sum(client_updates * client_data_size) / total_data_size

FedProx (Federated Proximal)

Best for: Non-IID data and system heterogeneity

loss = local_loss + (mu/2) * ||local_model - global_model||²

Privacy Features

Differential Privacy Levels

Privacy Level	Epsilon	Use Case
Minimal	ε > 10	Research/Testing
Standard	1 < ε < 10	Most applications
Strong	ε < 1	Highly sensitive data
Maximum	ε < 0.1	Medical/Financial data

Privacy Techniques

DP-SGD with gradient clipping
Noise injection
Secure aggregation
Client sampling

Integration & Extensibility

Built-in Support

PyTorch: Native PyTorch models
Ray: Distributed computing
OpenTelemetry: Observability
Prometheus/Grafana: Monitoring

Custom Extensions

Custom models
Custom datasets
Custom aggregation strategies
Custom topologies
Custom loss functions

Performance Characteristics

Scalability

Supports 10s to 1000s of clients
Linear scaling with client count
Ray cluster scaling

Communication Efficiency

Model update compression
Gradient pruning
Selective aggregation

Computation

Local training: PyTorch-based
Aggregation: Vectorized operations
Ray tasks for parallelization

Memory

VirtualNode for lazy initialization
Efficient gradient storage
Configurable batch sizes

Monitoring & Observability

Metrics Collected

Training accuracy and loss
Communication overhead
Model convergence
Privacy budget consumption
Resource utilization

Available Tools

Prometheus: Metric collection
Grafana: Visualization
Jaeger: Distributed tracing
OpenTelemetry: Instrumentation

Key References

Federated Learning Papers

“Communication-Efficient Learning of Deep Networks from Decentralized Data” (FedAvg)
“Federated Optimization in Heterogeneous Networks” (FedProx)
“Learning Differentially Private Recurrent Language Models” (DP-SGD)

Technologies

Ray: Distributed computing framework
PyTorch: Deep learning library
OpenTelemetry: Observability standard

Next Steps

Review Installation for setup
Read Getting Started for first training
Check Examples for practical implementations
Visit GitHub Repository for source code

Community & Support

GitHub Issues: Report bugs
Discussions: Ask questions
Documentation: Wiki