Cooperative Partial Task-Offloading for Heterogeneous Industrial Robotic MEC System Using Spectral and Energy-Efficient Federated Learning

Integrating distributed machine learning tools with sensing, communication, and decision-making operations in networked and cooperative intelligent machines has opened up novel avenues for research. The cross-fertilization of these components is essential for enabling collaborative task management that requires safety, reliability, scalability, and low latency. Data privacy preservation and high spectral efficiency are required for various Industrial Internet of Things (IIoT) applications. Most previous works have focused on joint optimization of task-offloading and resource allocation while neglecting the data privacy and data overhead of the decision-making process in task-offloading. Considering the robos as agents, this paper proposes Prioritized Spectral efficient Federated Reinforcement Learning (PSFRL)-based partial task-offloading in a heterogeneous industrial robotic Mobile Edge Computing (MEC) system. Due to its definition, PSFRL keeps data private while achieving high spectrum efficiency. Additionally, we define a Value of Information (VoI) metric so that the PSFRL agents allocate their resources more wisely for more valuable data. As robots have limited battery and computing resources for task processing, multiple edge computing-enabled access points assist the robots in accomplishing tasks. Simulation results show that our proposed PSFRL outperforms other learning-based task-offloading methods concerning energy consumption, spectral efficiency, and VoI. Moreover, we demonstrate the superiority of the proposed cooperative approach over its non-cooperative counterpart.