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.
