========
Overview
========

What is ICOS-FL?
----------------

ICOS-FL is a federated learning framework built on Flower for real-time resource monitoring
and prediction across distributed nodes. It enables organizations to train machine
learning models on system metrics data without centralizing sensitive information.

The framework uses LSTM (Long Short-Term Memory) neural networks to predict resource
utilization patterns based on historical data collected through OpenTelemetry and
stored in DataClay.

Architecture
------------

ICOS-FL consists of several components that work together:

- **SuperLink**: The central server component that orchestrates the federated learning process
- **SuperNodes**: Client components running on each node that collect metrics and train local models
- **DataClay**: A distributed object store that handles time series data
- **OTLP Bridge**: Connects OpenTelemetry metrics to DataClay for storage
- **LSTM Models**: Neural networks trained to predict resource usage patterns

Key Features
------------

- **Privacy-Preserving Learning**: Train models without sharing raw system metrics
- **Resource Prediction**: Forecast CPU, memory, and power usage in advance
- **Scalable Architecture**: Support for multiple nodes in a federated topology
- **Real-time Monitoring**: Track system metrics with minimal overhead
- **Docker Integration**: Easy deployment with containerized components

Use Cases
---------

ICOS-FL is designed for scenarios where organizations need to:

- **Predict Resource Spikes**: Anticipate CPU, memory, or power consumption surges
- **Optimize Resource Allocation**: Plan capacity based on predicted usage patterns
- **Detect Anomalies**: Identify unusual system behavior based on historical patterns
- **Maintain Data Privacy**: Keep sensitive system information within organizational boundaries

The framework is particularly useful for:

- **Edge Computing Environments**: Where data sovereignty is important
- **Multi-datacenter Deployments**: For aggregating insights without centralizing data
- **Privacy-Sensitive Organizations**: That need insights without exposing raw metrics