Authors - Akshay Kumar, Deepa Thilak Abstract - Smart city apps are growing quickly, which means that there are more real-time, latency-sensitive, and privacy-critical workloads that are hard for traditional single-cloud computing models to handle. In particular, smart mobility and traffic management systems generate large volumes of geographically distributed data that require efficient processing with minimal delay and high reliability. This project proposes a multi-cloud task scheduling framework that protects privacy and uses federated learning to solve these problems. The suggested system turns real-time smart mobility traffic data into abstract scheduling tasks and sends them to different cloud regions using a lightweight, decision-free task broker. Each cloud region has its own local federated scheduler that uses only data that is available in that region to schedule tasks based on latency and congestion. Federated learning is used to work together to improve scheduling policies by safely combining local model updates without sharing raw data. This keeps data private and meets data sovereignty requirements. The system enables improved scalability, reduced response time, fault tolerance, and avoidance of vendor lock-in compared to centralized scheduling approaches. Using a smart mobility dataset to test the proposed method shows that it works well for scheduling tasks quickly and with privacy in mind in multi-cloud settings.
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