Editing Emerging Research Directions (section)

====Decentralized & Distributed Methods====
There are several techniques in the decentralized and distributed methods side as well. Game theory models users as rational agents seeking equilibrium, providing practical yet possibly sub-optimal solutions. Matching theory is useful for binary offloading scenarios but struggles with more nuanced partial offloading. Auction-based models mirror economic systems, aligning task requests with resource availability. Meanwhile, federated learning addresses privacy and scalability by training models collaboratively across devices without sharing raw data, though it requires careful orchestration. Blockchain, another decentralized approach, ensures data integrity and trust in scheduling decisions but introduces significant latency, making it less suitable for real-time tasks. These decentralized strategies are increasingly critical as edge networks grow more heterogeneous and resource-constrained [4].