Federated Multi-Objective Meta-Reinforcement Learning for Adaptive Edge Task Offloading

With the proliferation of the Internet of Things (IoT) and mobile network technologies, efficient task offloading in edge computing has become pivotal for optimizing network resource allocation and enhancing data processing speed. However, edge task offloading for diverse applications of different users is typically multi-objective, where the complexity of multi-objective optimization presents significant challenges as wireless channel state and idle resources as well as the interference can change rapidly and the importance attached to different objectives by users may vary depending on the situation. Particularly in cases where the preference weights of these objectives fluctuate over time, traditional optimization techniques are typically unable to provide effective solutions. Moreover, the centralized training utilized by most Artificial Intelligence (AI)-based optimization algorithms raises concerns regarding the potential leakage of local private data to third parties. To address these challenges, we propose a novel federated multi-objective reinforcement learning (FMORL) algorithm, which employs a federated learning framework to perform collaborative learning on distributed nodes working in parallel, allowing for the fast and flexible acquisition of the optimal offloading strategy from dynamic environments, and introduces a meta-learning mechanism to enhance the fast adaptation of the model. Simulation experiments demonstrate that compared with the traditional MORL algorithm, the FMORL algorithm, embedded with meta-learning, improves the overall performance while preserving data privacy.