========================== Frequently Asked Questions ========================== General Questions ----------------- What is ICOS-FL? ~~~~~~~~~~~~~~~~ ICOS-FL is a federated learning framework built on Flower for resource monitoring and prediction across distributed nodes. It uses LSTM models to predict CPU, memory, and power usage patterns. How does ICOS-FL compare to other federated learning frameworks? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ICOS-FL is specifically optimized for time series prediction of system metrics. While general-purpose frameworks like TensorFlow Federated or PySyft offer broader ML capabilities, ICOS-FL provides a streamlined solution for resource monitoring with built-in DataClay integration. Does ICOS-FL require specialized hardware? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ No. ICOS-FL runs on standard hardware but can utilize GPUs if available for faster training. All components can run on CPU-only machines with reasonable performance. Technical Questions ------------------- How is data stored in ICOS-FL? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ICOS-FL uses DataClay as its persistent storage layer. Time series data is stored in a sliding window format, with the TimeSeriesData class handling automatic management of the buffer size. What metrics does ICOS-FL collect? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ By default, ICOS-FL collects CPU usage, memory usage, and power consumption through Scaphandre and OpenTelemetry. You can customize the metrics collection by modifying the BridgeConfiguration. Can I deploy ICOS-FL without Docker? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Yes, all components can be installed and run natively. However, Docker simplifies deployment and ensures consistent behavior across different environments. Federated Learning Questions ---------------------------- How many clients does ICOS-FL support? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ICOS-FL has been tested with up to 20 client nodes. The practical limit depends on network bandwidth, server resources, and aggregation strategy. How does ICOS-FL handle client failures? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ICOS-FL uses Flower's built-in fault tolerance mechanisms. If a client becomes unavailable during training, the server can continue with the remaining clients as long as the minimum number of available clients is satisfied. Can I use my own custom models? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Yes, ICOS-FL supports custom models as long as they implement the required interface. See :doc:`/how_to/models/custom_models` for details. Can ICOS-FL work with non-time-series data? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ While ICOS-FL is optimized for time series forecasting, its architecture can be adapted for other federated learning tasks. However, you would need to modify the data processing pipeline. Performance and Scaling ----------------------- How much data does ICOS-FL need for accurate predictions? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ For reasonable forecasting accuracy, ICOS-FL typically requires at least a few hours of historical data. Prediction accuracy improves with more data and training rounds. What is the network overhead of federated learning? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The network overhead is proportional to model size rather than data size. LSTM models in ICOS-FL are relatively compact (typically <1MB), making the communication efficient. How far ahead can ICOS-FL predict resource usage? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ By default, ICOS-FL is configured to predict 5 minutes ahead based on 15 minutes of historical data. This prediction horizon can be adjusted by modifying the time window configuration.