constraints. This non-convex problem is transformed with SCA and a general ratio iterative algorithm. In the future, we plan to generalize the chapter with multiple-antenna user/server and other access techniques. Wireless Systems for Distributed Machine Learning 9.1 Background Machine learning (ML) techniques especially deep learning have achieved remarkable performance in various applications such as object detection and content recommenda- tion. State-of-the-art ML exploits the growing computation power of mobile devices that are capable of collecting, sharing, and processing data. Although such devices are still resource-constrained compared with the high-performance computing (HPC) centers, crowd-sourcing a large number of them can build powerful ML models. Distributed ML requires devices to periodically share model attributes. Communication can become a severe challenge, especially when wirelessly connected devices participate in the distributed ML. Recently, federated learning (FL) is proposed to address this chal- lenge by selecting a portion of the devices during each round for updating. Study shows FL can achieve multi-fold performance benefits including improving communication effi- ciency, preserving privacy, as well as handling heterogeneous datasets [128]. From wireless connection perspective, NOMA has been recognized as a new access technology in 5G to improve spectrum efficiency. NOMA allows multiple users to share the same radio resource simultaneously. To mitigate the interference, SIC is applied at the receiver side. SIC starts the decoding for the signal with the strongest received power and subtracts the decoded sig- nal from the composite received message [229]. The process is sequentially carried out until the intended signals are all decoded. To further realize more efficient communication in FL, study in [106] found that 99.9% of the attributes exchanges are redundant and deep gradi- ent compression was applied to reduce the message size for transmission. Li et al. [103] proposed a modifi