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.

This review examines federated learning as an innovative approach enabling distributed machine learning while preserving data privacy. We explore recent developments addressing four core challenges: data heterogeneity across participants, optimizing communication between devices, strengthening privacy protections, and accommodating diverse system capabilities. Our analysis identifies substantial algorithmic progress while highlighting persistent gaps in practical implementations across varied devices, balancing privacy with model performance, and expanding applications in vertical federated learning contexts. The survey concludes with strategic recommendations for overcoming these limitations and accelerating real-world adoption of federated learning techniques, particularly focusing on scalability across heterogeneous environments, enhanced privacy-utility balance, and improved cross-organizational implementations that could substantially expand federated learning's practical impact.