Deployment of IBM Federated Learning Platform and Aggregation Algorithm Comparison: A Case Study Using the MNIST Dataset

• Hans Herbert Schulz CTJ / UFSC
• Benjamin Grando Moreira CTJ / UFSC

Resumo

With the exponential growth in artificial intelligence, privacy and data acquisition concerns have surged, prompting stricter data protection laws. Federated Learning (FL) addresses these issues by enabling model training without accessing private data, allowing geographically dispersed clients to participate without sharing data. This study deployed an IBM FL platform using Docker containers across two clients and conducted training with the MNIST dataset using the two most common FL strategies: Federated Stochastic Gradient Descent (FedSGD) and Federated Averaging (FedAvg). The results validated the platform’s deployment and assessed the performance of each strategy in terms of model accuracy and client hardware capabilities. Performance metrics, including CPU and RAM usage, network traffic, and model accuracy, were collected. Despite the higher resource demands, both strategies achieved satisfactory model accuracy, with FedAvg showing slightly better efficiency for the small-scale deployment. The results emphasize the potential of FL for predictive maintenance in industrial applications, enabling decentralized data utilization while ensuring data privacy and security.