Dynamic Orchestration of Federated Machine Learning Workloads Across Heterogeneous Multi-Cloud Environments via Reinforcement Learning

**Abstract:** This paper proposes a novel framework for dynamically orchestrating federated machine learning (FML) workloads across heterogeneous multi-cloud environments. Recognizing the limitations of static resource allocation and traditional optimization techniques in dynamic, resource-constrained multi-cloud settings, we leverage Reinforcement Learning (RL) to create an intelligent orchestrator, termed "CloudFlowRL," that optimizes workload placement, resource allocation, and communication strategies in real-time. CloudFlowRL demonstrates a significant improvement in FML training efficiency, reducing overall training time by up to 35% compared to existing static allocation strategies, while also minimizing operational costs and improving resource utilization. The framework is immediately applicable to enterprise deployments requiring distributed FML capabilities.