Published Paper Details:

To overcome the demerits of Cloud Computing as well as Edge Computing, the amalgamation of both the Cloud and Edge computing methodologies are consid- ered. The ongoing trend of collaboration of AI with IoT, called Artificial Intelligence of Things, comes into the picture as it helps to resolve delays in data processing, privacy, and security issues. Studies have provided various concerns that arise when the Cloud-Edge Terminal Collaboration with AIoT possesses some already discussed issues individually. This paper helps understand the issues in detail and helps to devise a plan to design framework architecture that surpasses the flaws or drawbacks of the existing model and also discusses two important ideas to con- template the feasible design. The first idea is Asynchronous Federated Learning with Quantization for Cloud-Edge-based computing. The second idea states us- ing Mobile Edge Computing with Blockchain that is built on a Block Coordinate Descent algorithm which focuses on solving Joint Mixed-Integer and non-convex task offloading schedules, radio spectrum resource allocation as well as computing resources allocation that minimizes the energy consumption and reduces the delay in the processing of data. Several other ideas exist to solve the ongoing problems by introducing AIoT in Cloud-Edge Computing which advises the use of Blockchain technologies to increase the security and privacy of data by authenticating Terminal devices and Differential Privacy concept in a parallel way to promote the sharing of only data features rather than the actual raw data.