Federated learning (FL) is a key solution to realizing a cost-efficient and intelligent Industrial Internet of Things (IIoT). To improve training efficiency and mitigate the straggler effect of FL, this paper investigates an edge-assisted FL framework over an IIoT system by combining it with a mobile edge computing (MEC) technique. In the proposed edge-assisted FL framework, each IIoT device with weak computation capacity can offload partial local data to an edge server with strong computing power for edge training. In order to obtain the optimal offloading strategy, we formulate an FL loss function minimization problem under the latency constraint in the proposed edge-assisted FL framework by optimizing the offloading data size of each device. An optimal offloading strategy is first derived in a perfect channel state information (CSI) scenario. Then, we extend the strategy into an imperfect CSI scenario and accordingly propose a Q-learning-aided offloading strategy. Finally, our simulation results show that our proposed Q-learning-based offloading strategy can improve FL test accuracy by about 4.7% compared to the conventional FL scheme. Furthermore, the proposed Q-learning-based offloading strategy can achieve similar performance to the optimal offloading strategy and always outperforms the conventional FL scheme in different system parameters, which validates the effectiveness of the proposed edge-assisted framework and Q-learning-based offloading strategy.

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Sixth generation (6G) wireless networks require very low latency and an ultra-high data rate, which have become the main challenges for future wireless communications. To effectively balance the requirements of 6G and the extreme shortage of capacity within the existing wireless networks, sensing-assisted communications in the terahertz (THz) band with unmanned aerial vehicles (UAVs) is proposed. In this scenario, the THz-UAV acts as an aerial base station to provide information on users and sensing signals and detect the THz channel to assist UAV communication. However, communication and sensing signals that use the same resources can cause interference with each other. Therefore, we research a cooperative method of co-existence between sensing and communication signals in the same frequency and time allocation to reduce the interference. We then formulate an optimization problem to minimize the total delay by jointly optimizing the UAV trajectory, frequency association, and transmission power of each user. The resulting problem is a non-convex and mixed integer optimization problem, which is challenging to solve. By resorting to the Lagrange multiplier and proximal policy optimization (PPO) method, we propose an overall alternating optimization algorithm to solve this problem in an iterative way. Specifically, given the UAV location and frequency, the sub-problem of the sensing and communication transmission powers is transformed into a convex problem, which is solved by the Lagrange multiplier method. Second, in each iteration, for given sensing and communication transmission powers, we relax the discrete variable to a continuous variable and use the PPO algorithm to tackle the sub-problem of joint optimization of the UAV location and frequency. The results show that the proposed algorithm reduces the delay and improves the transmission rate when compared with the conventional greedy algorithm.

Integrating digital twins (DTs) and multi-access edge computing (MEC) is a promising technology that realizes edge intelligence in 6G, which has been recognized as the key enabler for Industrial Internet of Things (IIoT). In this paper, we explore a DT-assisted MEC system for the IIoT scenario where a DT server is created as a digital counterpart of the MEC server, via estimating the computation state of the MEC server within the DT modelling cycle. To achieve energy and spectrally efficient offloading, we consider that IIoT devices communicate with industrial gateways (IGWs) through a non-orthogonal multiple access (NOMA) protocol. Each IIoT device has an industrial computation task that can be executed locally or fully offloaded to IGW. We aim to minimize the total task completion delay of all IIoT devices by jointly optimizing the IGW's subchannel assignment as well as the computation capacity allocation, edge association, and transmit power control of IIoT device. The resulting problem is shown to be a mixed integer non-convex optimization problem, which is NP-hard and challenging to solve. We decompose the original problem into four solvable subproblems, and then propose an overall alternating optimization algorithm to solve the sub-problems iteratively until convergence. Validated via simulations, the proposed scheme shows superiority to the benchmarks in reducing the total task completion delay and increasing the percentage of offloading IIoT devices.

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