Information Recycling Assisted Collaborative Edge Computing for Distributed Learning

The ever-increasing scale and complexity of artificial intelligent services have ignited the recent research interests in distributed edge learning. For better communication rate and spectral efficiency, non-orthogonal transmissions are often adopted for distributed edge learning. On the other hand, the computation rate of distributed edge earning is sometimes hampered by a few straggling edge nodes (ENs) and existing countermeasures either introduce redundant computation or require extra data retransmission. To the best of our knowledge, developing a new edge computing scheme for distributed learning that can handle EN straggling without these extra costs still remains open. Fortunately, it is found in this work that this computation issue can be addressed jointly with the communication issue by integrating a novel information recycling mechanism into existing non-orthogonal transmission techniques. In particular, an information recycling assisted collaborative edge computing scheme is proposed in this work for distributed learning, which allows each EN to recycle part of the task information intended for other ENs for free, by exploiting the successive interference cancellation (SIC) procedure in non-orthogonal transmission. In this way, faster ENs can help execute part of the workload of the straggling ENs without redundant computation and data retransmission. Besides, to optimize the corresponding total throughput of the distributed edge learning system, a joint power control and rate splitting algorithm is developed. Simulations are conducted to corroborate the effectiveness of the proposed scheme.