FinBERT-FedProx

FinBERT–FedProx: Federated Proximal Optimization for Financial Sentiment Analysis

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📌 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.

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🧠 Intended Use

Designed for:

• Financial sentiment research
• Risk & market analytics
• Academic exploration of federated learning

Not intended for automated trading without expert oversight.

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🏗 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)

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📊 Client Data Sources

No raw data is shared between clients.

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🔐 Privacy Advantage

Only model updates are exchanged — not text data.
This supports data governance and privacy-aware ML.

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📈 Performance (Validation)

FedProx provided slightly better stability and performance compared to standard FedAvg under client data imbalance.

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🚀 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)

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⚠️ Limitations

• Trained only on finance-domain text
• Sentiment ≠ market prediction
• Model may inherit dataset biases
• Designed for research use

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📚 Citation

Harsh Prasad, Sai Dhole (2025).
FedProx-based Federated FinBERT for Financial Sentiment Analysis.

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👨‍💻 Authors

Harsh Prasad AI and ML Research

Sai Dhole AI and ML Research

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