FinBERT–FedAvg: Federated Averaging 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
This model is trained using the Federated Averaging (FedAvg) algorithm, where each client trains locally on its own data and only model weights are shared. No raw data is exchanged, supporting privacy-preserving learning.
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
FedAvg 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 |
|---|---|
| FedAvg | 0.846 |
FedAvg provided strong and stable global performance across heterogeneous financial text sources.
🚀 Example Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained(
"harshprasad03/FinBERT-FedAvg"
)
tokenizer = AutoTokenizer.from_pretrained(
"harshprasad03/FinBERT-FedAvg"
)
text = "Tech stocks fell after negative earnings guidance."
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, Sai Dhole (2025).
FedAvg-based Federated FinBERT for Financial Sentiment Analysis.
👨💻 Authors
Harsh Prasad AI and ML Research
Sai Dhole AI and ML Research
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