Advancing Federated Learning: Optimizing Model Accuracy through Privacy-Conscious Data Sharing
Rihab Saidi, Tarek Moulahi, Suliman Aladhadh, Salah Zidi · 2024
Our innovative federated learning approach addresses the evolving landscape of collaborative machine learning by strategically sharing $80 \%$ of the dataset among decentralized devices. Utilizing TensorFlow Federated and integrating privacy-preserving techniques like differential privacy, our framework seeks to harmonize enhanced model accuracy with robust privacy preservation (20%). Through extensive experiments, we showcase the significant improvement in model accuracy (98.53%) compared to traditional federated learning (97.55%). Analyzing trade-offs between accuracy and privacy preservation, we offer insights into the impact of varied data partitioning ratios and privacy-preserving parameters. This contribution presents an effective methodology for privacy-conscious collaborative model training, unveiling opportunities to optimize federated learning processes and emphasizing the delicate balance between model performance and privacy in collaborative machine learning environments.