Update README.md

README.md

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-license: mit
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+## FinBERT–AdaptiveFedAvg: Adaptive Federated Aggregation for Financial Sentiment Analysis
+
+---
+
+### 📌 Model Summary
+
+This model is a **federated version of FinBERT** fine-tuned for
+**financial sentiment classification (Positive / Negative / Neutral)**.
+
+Training is performed across **three clients**:
+
+* Financial Twitter posts
+* Financial news headlines
+* Financial reports & statements
+
+Unlike standard FedAvg, this model uses an **Adaptive Aggregation strategy**,
+where client contributions are **weighted dynamically based on validation performance**,
+allowing stronger clients to influence the global model more.
+
+This model is part of a research project comparing:
+
+* FedAvg
+* FedProx
+* Adaptive Aggregation
+
+for federated financial NLP.
+
+---
+
+### 🧠 Intended Use
+
+Designed for:
+
+* Financial sentiment research
+* Risk & market analytics
+* Academic exploration of federated learning
+
+Not intended for automated trading without expert oversight.
+
+---
+
+### 🏗 Model Architecture
+
+Base Model:
+
+```
+ProsusAI/finbert
+```
+
+Task:
+
+```
+Sequence classification — 3 classes
+```
+
+Training Setup:
+
+```
+3 federation clients
+10 global rounds
+3 local epochs
+Adaptive weighted aggregation
+```
+
+---
+
+### 📊 Client Data Sources
+
+| Client   | Data Type         |
+| -------- | ----------------- |
+| Client-1 | Financial Twitter |
+| Client-2 | Financial News    |
+| Client-3 | Financial Reports |
+
+No raw data is shared between clients.
+
+---
+
+### 🔐 Privacy Advantage
+
+Only model updates are exchanged — not text data.
+This supports data governance and privacy-aware ML.
+
+---
+
+### 📈 Performance (Validation)
+
+| Method          | Final Avg F1-Score |
+| --------------- | ------------------ |
+| Adaptive FedAvg | **0.823**          |
+
+Adaptive aggregation showed **smooth convergence and stable performance**
+while preserving privacy.
+
+---
+
+### 🚀 Example Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model = AutoModelForSequenceClassification.from_pretrained(
+    "harshprasad03/FinBERT-Adaptive"
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "harshprasad03/FinBERT-Adaptive"
+)
+
+text = "Global markets improved after positive earnings reports."
+
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+
+prob = torch.softmax(outputs.logits, dim=1)
+print(prob)
+```
+
+---
+
+### ⚠️ Limitations
+
+* Trained only on finance-domain text
+* Sentiment ≠ market prediction
+* Model may inherit dataset biases
+* Designed for research use
+
+---
+
+### 📚 Citation
+
+```
+Harsh Prasad (2025).
+Adaptive Federated FinBERT for Financial Sentiment Analysis.
+```
+
+---
+
+### 👨‍💻 Author
+
+**Harsh Prasad**
+Federated Learning & NLP Research
+
+---