This NSF ERI project aims to make collaborative machine learning more practical for real-world edge systems where data are distributed across devices, and networks often differ in speed, reliability, and computing capability. Today, many distributed learning methods require each device to train a full neural network or to exchange large amount of information during training, which can be costly for edge devices such as wearables, mobile devices, and other resource-limited platforms. The project will develop a new class of split learning methods that avoid the heavy communication required by standard back propagation (BP), while still allowing devices and servers to train models together without sharing raw data. This will be achieved by replacing repeated gradient exchanges with lightweight scalar updates that are better suited for heterogeneous multi-edge environments. The intellectual merit of the project includes establishing the theoretical foundations, algorithmic designs, and evaluation methods needed for back propagation-free split learning in distributed systems. The broader impacts of the project include expanding access to advanced machine learning for organizations and communities with limited computing and networking resources, supporting education and workforce development through research-integrated training, and releasing open-source tools and benchmarks that can benefit the broader research and education communities. Technically, this project studies how to