language: en
license: mit
tags:
- federated-learning
- finance
- sentiment-analysis
- bert
- finbert
- fedprox
library_name: transformers
pipeline_tag: text-classification
authors:
- Harsh Prasad
- Sai Dhole
FinBERT–FedProx: Federated Proximal Optimization 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 FedProx optimization, which adds a proximal penalty term to stabilize client training when data across clients is non-identically distributed (non-IID).
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
FedProx (µ = 0.05)
📊 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 |
|---|---|
| FedProx | 0.855 |
FedProx provided slightly better stability and performance compared to standard FedAvg under client data imbalance.
🚀 Example Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained(
"harshprasad03/FinBERT-FedProx"
)
tokenizer = AutoTokenizer.from_pretrained(
"harshprasad03/FinBERT-FedProx"
)
text = "Oil stocks rose after strong quarterly performance."
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).
FedProx-based Federated FinBERT for Financial Sentiment Analysis.
👨💻 Authors
Harsh Prasad AI and ML Research
Sai Dhole AI and ML Research