Federated Learning in Distributed Cloud Environments: Communication Efficiency, Differential Privacy Guarantees, and Model Convergence Under Non-IID Data Distributions
Federated learning enables collaborative model training across distributed clients without centralizing raw data, making it particularly valuable in regulated industries where data sharing is legally constrained. However, the practical deployment of federated learning in production cloud environments exposes significant engineering challenges: communication overhead from model update aggregation, degraded convergence under non-IID (heterogeneous) data distributions, and the tension between differential privacy noise injection and model utility. This paper presents FedCloud, a production-oriented federated learning framework designed for multi-organization deployment across cloud environments, and reports its empirical evaluation across three deployment scenarios: cross-hospital clinical NLP, cross-bank fraud detection, and cross-retailer demand forecasting. FedCloud implements adaptive FedAvg with gradient compression (achieving 7.3x communication reduction), client selection strategies optimized for stragglers in cloud environments, and Renyi differential privacy with per-round epsilon tracking. Under non-IID distributions simulating real organizational data heterogeneity, FedCloud achieves within 2.8% of centralized training accuracy for the clinical NLP task and within 4.1% for fraud detection, while satisfying epsilon less than 3 per training round. We characterize the accuracy-privacy-communication three-way trade-off surface empirically and provide a configuration selection guide for practitioners deploying federated learning under specific regulatory and performance constraints.