harshprasad03/FinBERT-Adaptive

FinBERT–AdaptiveFedAvg: Adaptive Federated Aggregation 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 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.

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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
Adaptive weighted aggregation

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📊 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.

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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)

Method	Final Avg F1-Score
Adaptive FedAvg	0.823

Adaptive aggregation showed smooth convergence and stable performance while preserving privacy.

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🚀 Example Usage

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)

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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).
Adaptive 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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