Authors - Sanchi Mahajan, Nandini Jain, Evangelin G, Jansi K R, Shivam Shivam Abstract - The issue of efficient work planning in heterogeneous multi-cloud in frastructures is still an open issue due to scalability limitations, data privacy, and latency sensitivity. The conventional centralized scheduling approach requires data aggregation, which is associated with critical privacy challenges and com munication cost. The proposed work aims to design a privacy-preserving feder ated multi-cloud task scheduling framework for smart mobility applications to overcome the limitations of conventional approaches. The proposed framework employs a decentralized scheduler for separate cloud regions. The proposed framework employs a novel task abstraction approach to transform real-time traffic data into task-scheduling forms. The proposed framework eliminates the requirement to communicate raw traffic data by employing a federated learning based aggregation approach. The proposed framework employs a federated ag gregation approach, which is associated with scalability, routing, and multi cloud coordination while ensuring data locality. The proposed framework is evaluated by conducting experiments on Random, Rule-Based, Local-ML ap proaches using a Smart Mobility dataset. As can be observed from the results, considerable reductions in communication overhead and privacy leakage are achieved with the preservation of competitive execution latency and SLA com pliance. The strategy has been observed to scale well with an increase in cloud regions, as the communication scalability results indicate. It is the ability to sup port federated, scalable, and privacy-aware job scheduling for smart traffic sys tems without central data sharing that makes this work interesting.
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