## 1 Introduction
In response to the energy consumption optimization efficiency problem of traditional optimization algorithms in the Industrial Internet scenario with multiedge-terminal collaboration, we design a computing offloading and resource allocation algorithm utilizing deep deterministic policy gradient (DDPG) [15] combined with FL [16]. By jointly optimizing task offloading ratio, subcarrier quantity, transmission power, and computing frequency, a better strategy can be obtained by this algorithm, thus reducing the system's overall energy consumption. This paper makes the following primary contributions:  
* This article examines the resource allocation and computing offloading of multiedge-terminal collaboration within the MEC for an Industrial Internet intelligent factory. In this situation, work from terminal devices can be delegated to a single edge server or a group of edge servers simultaneously. Tasks are also capable of being computed locally on terminal devices. Task offloading has the potential to be either full offloading or partial offloading to the edge servers. * Our focus is on enhancing the energy efficiency of conventional algorithms, and we are exploring ways to lower the system's entire usage of energy. Specifically, we are investigating how multiedge device collaboration can be leveraged to optimize computing offload and resource allocation. We transform the resource allocation and computing offloading combined optimization issue into a Markov decision process (MDP) problem. To minimize energy consumption, we want to optimize the MEC system. We plan to accomplish this by optimizing various system variables, including the task offloading ratio, subcarrier quantity, transmission power, and computing frequency, while satisfying the constraint of task completion delay. * A novel computing offloading and resource allocation algorithm due to DDPG and FL is introduced in this work. DDPG is an effective DRL algorithm with two deep neural networks. FL is a decentralized machine learning technique that makes devices to learn from each other without sharing their local data. We integrate a federatedlearning approach into the policy network and value network of the conventional DDPG algorithm. Following gradient updates, we upload the neural network parameters from multiple DDPG algorithms to a cloud node for federated averaging. Subsequently, these parameters are distributed back to the neural networks of each DDPG algorithm to facilitate the updating of their respective neural network parameters. Combining the DDPG algorithm with FL enables the multiuser system in the Industrial Internet networks to achieve a better strategy for computing offloading and resource allocation with lower overall usage of energy while maintaining computing efficiency. To provide structure and clarity to the remainder of this paper, we organized it in the following manner. In Section 2, we present related work in the background of the article. In Section 3, we provide the system model and the optimal solution challenge. In Section 4, to tackle the optimization problem we are addressing in this paper, we introduce a computing offloading and resource allocation algorithm. In Section 5, we test and verify the proposed algorithm's performance and compared it against the baseline to assess its effectiveness. Section 6 gives the conclusions of this paper.