## 4 Literature Review and Data Extraction
### Distributed Task Allocation
Overall, the suggested approach offers a better method for handling multi-skill mobile crowdsourcing jobs, improving the process efficiency and fairness. A hierarchical fog-computing cloud radio access network (CRAN) architecture that combines fog access points (FAPs) with MEC servers to reduce task offloading's wait time was published by [PERSON] et al. [61]. The system includes multiple levels of MEC-L servers in radio units (RUs), MEC-H servers in distributed units (DUs), and a cloud computing service in central units (CUs), which helps decrease the distance needed for offloading computational tasks and improving the delay performance for computation-intensive and delay-sensitive applications like virtual reality and automated driving. The authors suggest a mixed-integer nonlinear programming problem aimed at minimizing delay by optimizing the distribution of jobs, the transmission beamforming vectors, the processing speed, and the allocation of transmission bandwidth. They loosened the integer restrictions to convert the issue into a convex problem and solved it using the Lagrange dual approach. The hierarchical C-RAN network based on fog computing that was suggested outperforms current networks in terms of latency performance, according to the simulation findings. The suggested structure enables access to pooled processing power and storage resources, which can assist in managing a sizable number of offloading requests and saving operational expenses. A recent work [63] discusses an FL method that uses attention to handle the collaborative task allocation (CTA) issue in contexts with edge-assisted smart grids. The CTA challenge entails improving both user equipment task completion delay and energy usage while taking subtask dependencies into consideration. The authors offer an attentive approach to extract information from both the power UE and the power base and characterize the relationships as a directed acyclic graph (DAG). The attention-aided FL method was then suggested in order to determine the best course of action for the CTA issue as an MDP. The suggested method outperforms existing options, according to the simulation findings. The suggested method is anticipated to increase the data accuracy and efficacy in situations involving smart grids, as well as data privacy and security during multi-party data sharing. [PERSON] et al. [31] suggested that the on-time execution of a distributed, resource-intensive activity requires a method to perform the decentralized grouping of edge nodes. The authors offered a mathematical framework for distributing resources fairly among tasks in distributed edge execution environments. Nodes in the edge network have their reliability characteristics which were assessed. An effective decentralized fault tolerance mechanism is offered for heterogeneous edge networks that are prone to errors. Furthermore, in this work, an algorithm for backup nodes based on a dependability model that offers redundancy was selected and used. Finally, they thoroughly evaluate the suggested strategies to confirm their effectiveness. Prior research [33] suggests that a graph-based combinatorial optimization problem can be employed to simulate a distributed deployment classifier. Even though the greedy approach can obtain a reasonable approximation of the answer, real-time systems have trouble keeping up with its slow response time. Moreover, an reinforcement learning (RL)-based classifier deployment technique was provided that uses graph convolutional network to learn network structure details in order to choose the best modification strategy for the given circumstance at each step until all nodes are chosen. The in-depth simulation tests showed off the effectiveness of their technique. First, they demonstrated a decent approximation solution and greatly minimized the computation time by comparing their approach to the greedy one. Second, a demonstration of contrasting distributed throttle techniques using a classifier yielded superior results.