In this chapter, we presentParameter distribution a three-layer (i.e., device, field, and factory layers) deterministicSoft update federatedHard update learningExploration vs exploitation frameworkBellman equation, namedReward shaping DetFed, whichState-action-reward (SAR) acceleratesStream reservation protocol (SRP) collaborative learning process for ultra-reliable and low-latency industrial Internet of Things via integrating 6G-oriented time-sensitive networks. Utilizing dispersive local data, IIoT devices distributively train a DNN model, and the updated model parameters are aggregated at their associated field servers every round or at a centralized factory server every a few rounds. Aiming at optimizing the learning accuracy of FL without affecting the co-transmission of burst traffic (e.g., safety-critical traffic), an integrated TSNTime-sensitive networks (TSN) is considered to establish connections among the three layers, where a cyclic queuing and forwarding mechanism is deployed in each switch to support deterministic model parameter transmission with microsecond-level delay and near-zero packet loss requirements. To improve the FL performance, we formulate a multi-objective stochastic optimization problem to simultaneously maximize the scheduling success ratio and learning accuracy while satisfying the deterministic requirements of delay, jitter, and packet loss. Since the objective function is implicit and the available time slots of the considered TSN in each FL round are temporally correlated, the problem is difficult to solve in real time. Therefore, we transform the problem into a Markov decision process formulation and propose a dynamic resource scheduling algorithm, based on DRLDeep Reinforcement Learning (DRL), to make optimal resource scheduling decisions while adapting to device heterogeneity and network dynamics. Experimental results based on real-world dataset demonstrate that the proposed DetFed significantly accelerates FL convergence and improves learning accuracy as compared to state-of-the-art benchmarks.