Recently, artificial intelligence approaches are widely suggested to optimize numerous offloading task-scheduling purposes. However, they confront difficulties in maintaining data privacy regarding the context of the data offloading during the course of offloading in the different stages. To address this problem, in this article we propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C-fDRL</monospace> , a framework to provide context-aware federated deep reinforcement learning (fDRL) to maintain the context-aware privacy of the task offloading. We perform this in three stages (CloudAI, EdgeAI, and DeviceAI) of the overall system. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C-fDRL</monospace> checks whether the privacy of high-context-aware data with the task being offloaded is maintained locally at the DeviceAI, and low-context-aware data distributedly at the EdgeAI. When there is an offloading task request or a user needs to offload the data, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C-fDRL</monospace> uses a context-aware data management approach to decouple the context-aware (privacy) data from the tasks. This separates the context-aware data from the task for local computation and allows a new scheduling technique called “context-aware multilevel scheduler.” This places high-context-aware data on local devices and low-context-aware data at the edge device for computation before the actual task execution. We performed experiments to evaluate the data privacy with the offloading tasks and the federated DRL. The results show that the proposed <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C-fDRL</monospace> performs better than the existing framework.