Hardware-Aware Federated Learning: Optimizing Differential Privacy in Distributed Computing Architectures

This paper analyzes hardware-aware federated learning implementation with differential privacy optimization. Experiments across 10 distributed clients using MNIST show that DP-FedAvg achieves 89.2% accuracy with privacy guarantees (e = 0.20), representing only a 5% reduction compared to standard FedAvg. Our hardware analysis identifies 15–25% increased memory usage and 30–40% computational variation across devices, while communication costs scale linearly up to 1000 clients. Implementation across heterogeneous platforms demonstrates an effective balance between privacy and performance in resource-constrained environments, providing practical deployment guidelines for privacy-preserving federated learning systems.