Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned Edge Learning Over Broadband Channels

A main edge learning paradigm, called partitioned edge learning (PARTEL), is considered. It supports the distributed training of a large-scale AI model by dynamically partitioning the model and allocating the resultant parametric blocks to different devices for updating. Then devices upload the updates to a server where they are assembled and applied to updating the model. The two steps are iterated till the model converges. In this work, we consider the efficient joint management of parameter allocation and radio resources to reduce the learning latency of PARTEL, when deployed in a broadband system using orthogonal frequency-division multiplexing (OFDM). Specifically, the policies for joint subcarrier, parameter, and power allocation (SUPPORT) are optimized under the criterion of minimum latency. Two cases are considered. First, for the case of decomposable models (e.g., logistic regression or support vector machine), the latency-minimization problem is a mixed-integer program and non-convex. Due to its intractability, we develop a practical solution by 1) relaxing the binary subcarrier-assignment decisions and 2) transforming the relaxed problem into a convex problem of model size maximization under a latency constraint nested in a simple search for the target model size. By deriving the properties of the convex problem, a low-complexity algorithm is designed to compute the SUPPORT policy. Second, consider the case of convolutional neural network (CNN) models which can be trained using PARTEL by introducing some auxiliary variables. This, however, introduces constraints on model partitioning reducing the granularity of parameter allocation. The preceding policy is extended to CNN models by applying the proposed techniques of load rounding and proportional adjustment to rein in latency expansion caused by the load granularity constraints.