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.
