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+---
+license: mit
+---
+
+# RAMPART-FL: A Dataset for Offline Reinforcement Learning in Federated Participant Selection
+
+## Dataset Summary
+
+This repository contains a dataset package generated by the **RAMPART-FL** framework, a system for researching Reinforcement Learning (RL) based participant selection in Federated Learning (FL) for intrusion detection. The data originates from a **400-round, 25-client simulation** where an RL agent used a Multi-Criteria strategy to select clients.
+
+This package provides two distinct files to serve different research needs:
+1.  **`rampart_fl_400r_25c_multicriteria_event_log.csv`**: A detailed, event-driven log that is ideal for in-depth analysis, custom feature engineering, and understanding the step-by-step behavior of the FL process.
+2.  **`rampart_fl_400r_25c_multicriteria_rl_transitions.csv`**: A pre-processed, analysis-ready dataset of `(State, Action, Reward, Next_State)` tuples that has been enriched with contextual client and reward information.
+
+The scripts used to generate these files from the raw experimental output are available in the associated GitHub repository.
+
+## Associated Research
+
+This dataset is a key contribution of a master's thesis focused on creating robust and adaptive security solutions for IoT networks using Federated Learning.
+
+*   **Thesis & Framework:** [RAMPART-FL on GitHub](https://github.com/LCS-FS/RAMPART-FL)
+*   **Hugging Face Dataset:** [RAMPART-FL on Hugging Face](https://huggingface.co/datasets/LCS-FS/RAMPART-FL)
+
+---
+
+## File 1: The Refined Event Log (`rampart_fl_400r_25c_multicriteria_event_log.csv`)
+
+This file provides a detailed log of all relevant events that occurred during the simulation. It is best suited for researchers who need to perform detailed analysis beyond standard RL transition data.
+
+The dataset is structured in a sparse, event-driven format. This means that for any given row, **only the columns relevant to its `event_type` will contain data**. All other columns in that row will be empty.
+
+### Event Log Columns
+
+| Column Name | Description | Data Type |
+| :--- | :--- | :--- |
+| **Core Identifiers** | | |
+| `elapsed_seconds_since_start` | Time in seconds since the simulation began. | Float |
+| `server_round` | The FL training round number. | Integer |
+| `event_type` | The type of event being logged (`selection_info`, `learning_update`, etc.). | String |
+| `client_cid` | The unique identifier for the client. | Integer (Large) |
+| **State, Action & Policy (`selection_info`)** | | |
+| `s_client_state_tuple` | The state vector \(S\) representing the client's condition. | String |
+| `s_was_selected` | The action \(A\) taken by the agent (`1` or `0`). | Integer |
+| `s_available_cids_count`| The number of clients available for selection. | Integer |
+| `s_client_q_value` | The Q-value of the state-action pair from the original agent. | Float |
+| `s_client_selection_prob`| The selection probability from the original agent's policy. | Float |
+| **Reward & Learning (`learning_update`)** | | |
+| `l_state_at_selection` | The state \(S\) for which the reward is being applied. | String |
+| `l_reward_for_action` | The total global reward \(R\) for the round's actions. | Float |
+| `l_reward_*_component` | The different components (performance, fairness, etc.) of the total reward. | Float |
+| **Client Evaluation Metrics (`client_eval_metrics`)**| | |
+| `c_eval_partition_id`| The client's static data partition ID. | Integer |
+| `c_eval_profile_name` | The client's hardware profile name. | String |
+| `c_eval_cores` | Number of CPU cores available to the client. | Integer |
+| `c_eval_f1`, `_accuracy`, etc. | The client's local evaluation performance metrics. | Float |
+| `c_eval_num_samples` | Number of samples in the client's local test set. | Integer |
+| **Client Fit Metrics (`client_fit_metrics`)**| | |
+| `c_fit_time_seconds` | Time taken for the client's local training task. | Float |
+| `c_fit_cpu_percent`| The client's average CPU usage during training. | Float |
+
+---
+
+## File 2: Enriched RL Transitions Dataset (`rampart_fl_400r_25c_multicriteria_rl_transitions.csv`)
+
+This file provides a clean, analysis-ready dataset where each row is a complete, enriched `(State, Action, Reward, Next_State)` tuple. It is designed for direct use in most offline RL training libraries and workflows. Contextual columns (like `c_eval_f1`) will only contain data if the `action` for that row was `1`.
+
+### Transitions Dataset Columns
+
+| Column | Data Type | Description |
+| :--- | :--- | :--- |
+| **Core Transition** | | |
+| `server_round` | Integer | The server round `N` in which the state was observed and the action was taken. |
+| `client_cid` | String | The unique identifier for the client. |
+| `state` | String | The state vector \(S_t\) representing the client's condition at the start of round `N`. |
+| `action` | Integer | The action \(A_t\) taken for the client: `1` if selected, `0` otherwise. |
+| `reward` | Float | The global reward \(R_{t+1}\) received after the completion of round `N`. |
+| `next_state` | String | The subsequent state vector \(S_{t+1}\) for that same client at the start of round `N+1`. |
+| **Reward Components** | | |
+| `l_reward_performance_component` | Float | The portion of the reward attributed to the global model's performance. |
+| `l_reward_fairness_penalty_component`| Float | The portion of the reward attributed to the fairness penalty. |
+| `l_reward_resource_cost_component`| Float | The portion of the reward attributed to the resource cost of selected clients. |
+| **Client Context (for the round)** | | |
+| `c_eval_profile_name` | String | The client's hardware profile name (e.g., `High-End Edge CPU`). |
+| `c_eval_cores` | Float | Number of CPU cores available to the client (Float due to possible NaNs). |
+| `c_eval_f1` | Float | The client's local F1-score from its evaluation in this round. |
+| `c_eval_num_samples` | Float | Number of samples in the client's local test set. |
+| `c_fit_time_seconds` | Float | Time taken for the client's local training in this round. |
+| `c_fit_cpu_percent`| Float | The client's average CPU usage during training. |