Scalable, privacy-preserving, cost-effective, and dynamic workload-tolerant cloud resource management is needed. The federated and decentralized framework uses deep learning, reinforcement learning, edge–fog collaboration, transformer-based forecasting, and anomaly-aware post-processing. Together, these components provide proactive load balancing, adaptive provisioning, and increased service reliability across distributed cloud ecosystems. The architecture allows collaborative scheduling without regional workload data, cost-aware pre-emptive resource decisions, edge–fog nodes for latency-critical workloads, and transformer-based attention modeling for precise workload forecasts. Multi-regional deployment improves forecast accuracy, resource usage, latency, and SLA compliance in large-scale Google and Alibaba cloud traces. System SLA adherence is above 98%, accuracy above 95%, resource utilization above 25%, and latency below 30%. Comparative assessments demonstrate anomaly response and operational cost savings gains. The findings stress federated learning, decentralized scheduling, and cost-aware optimization. The architecture enables varied infrastructures, variable demand, and changing market conditions, enhancing system resilience. The research proposes leveraging the model to build next-generation cloud management systems with high adaptability, privacy assurances, and cheap overheads. Structured model design, operational process, and assessment methodologies enable reproducibility and expansions.
Shingne et al. (2026) studied this question.