Explanation

This section provides in-depth explanations of ICOS-FL’s architecture and key concepts.

• Architecture Overview
  • Core Components
  • Component Interactions
  • Cross-Cutting Concerns
  • Deployment Models
• Deployment Architecture
  • Deployment Patterns
  • Single-Machine Deployment
  • Federated Deployment
  • Component Communication
  • Docker Container Architecture
  • Network Configuration
  • Resource Requirements
  • Deployment Considerations
• Dataflow Pipeline
  • Overview
  • Stage 1: Metrics Collection
  • Stage 2: Metrics Processing
  • Stage 3: Bridge Layer
  • Stage 4: Data Storage
  • Stage 5: Data Preprocessing
  • Stage 6: Model Training
  • Complete Data Lifecycle
• Federated Learning Basics
  • What is Federated Learning?
  • Federated Learning Process
  • Why Use Federated Learning?
  • Federated Learning in ICOS-FL
  • ICOS-FL Client Lifecycle
  • Federation Challenges
  • Common Patterns
  • Privacy and Security
• Federated Learning Strategies
  • Federated Averaging (FedAvg)
  • Advanced Strategies
  • Handling Non-IID Data
  • Performance Considerations
  • Evaluation and Metrics
• LSTM Fundamentals
  • Introduction to LSTM Networks
  • LSTM Architecture
  • LSTM Implementation in ICOS-FL
  • Why LSTM for Time Series Prediction
  • Batching and Sequence Creation
  • Training Process
  • LSTM Advantages for ICOS-FL
  • Limitations and Considerations
• Sequence Prediction
  • Time Series Prediction Overview
  • Input Data Characteristics
  • Sequence Formation
  • Data Preprocessing
  • Single-Step vs. Multi-Step Prediction
  • Model Evaluation
  • Prediction Challenges
  • Practical Applications
  • Federated Learning Benefits