Update README.md

README.md

@@ -2,4 +2,69 @@
 license: mit
 language:
 - en
----
+task_categories:
+- anomaly-detection
+- image-classification
+- time-series-classification
+multilinguality:
+- monolingual
+---
+
+# FedJam Dataset
+
+**Multimodal Dataset for Federated Jamming Detection**
+
+The **FedJam dataset** is a multimodal dataset designed for **jamming detection and classification in wireless networks**, pairing **time–frequency spectrogram images** with **cross-layer network KPI time series**. The dataset is designed to support **multimodal learning**, **federated learning**, and **robust classification under heterogeneous data distributions**.
+
+Each sample contains **aligned vision and time-series modalities**, enabling joint modeling of physical-layer signal characteristics and network-layer performance indicators.
+
+---
+
+## Dataset Overview
+
+- **Modality 1 (Vision)**: Spectrogram images  
+  - Format: PNG  
+  - Resolution: **224 × 224 × 3**
+
+- **Modality 2 (Time Series)**: Network KPIs  
+  - Format: CSV / structured sequences  
+  - Multivariate time series with features:
+    - `Time`
+    - `Latency`
+    - `Jitter`
+    - `Packet Loss Count`
+    - `Noise`
+    - `SNR`
+
+- **Labels**: Benign traffic and multiple jamming attack types
+
+The dataset is designed to be **federated-learning friendly**, with samples optionally grouped by **client/device identifiers** to emulate decentralized data ownership.
+
+---
+
+## Dataset Statistics
+
+*(Replace X, Y, Z with the final numbers.)*
+
+| Subset | Train | Test | Total |
+|------|------:|-----:|------:|
+| FedJam-Sample | X | Y | Z |
+| **Total** | **X** | **Y** | **Z** |
+
+---
+
+## Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load the FedJam dataset
+dataset = load_dataset("YOUR_ORG_OR_USERNAME/fedjam")
+
+# Access splits
+train_data = dataset["train"]
+test_data  = dataset["test"]
+
+# Access a sample
+sample = train_data[0]
+print(sample.keys())