A Differentially Private Offloading Mechanism for Federated Learning

This paper proposes a task offloading mechanism based on differential privacy and federated learning. The proposed model adds noise to data streams, with the aim of protecting workload information in cloud computing from being leaked. Firstly, we analyzed the workload of a single server at different time points and used the method of noise adding to protect the data in order to observe the impact of protection on the data. Next, we extend our analysis to consider the average workload of all servers in the entire computing system, observing system performance from a macro perspective. Additionally, we study how different levels of noise impact the workload of a single server and generalize it to evaluate their impact on overall system performance. Finally, we analyze the workload of a single server at maximum noise level and calculate variance in system workload at different noise levels to evaluate its impact on system stability. In general, this protection method adopts differential privacy technology which effectively protects each machine's workload in cloud computing from being leaked. It not only prevents potential leakage of workload data but also ensures privacy and security during data transmission. The application of this method will promote development towards more secure and reliable cloud computing services, providing users with a more secure experience for their data services.