Quantization Avoids Saddle Points in Distributed Optimization

arXiv:2403.10423v1 Announce Type: cross
Abstract: Distributed nonconvex optimization underpins key functionalities of numerous distributed systems, ranging from power systems, smart buildings, cooperative robots, vehicle networks to sensor networks. Recently, it has also merged as a promising solution to handle the enormous growth in data and model sizes in deep learning. A fundamental problem in distributed nonconvex optimization is avoiding convergence to saddle points, which significantly degrade optimization accuracy. We discover that the process of quantization, which is necessary for all …

abstract arxiv buildings cs.lg data deep learning distributed distributed systems growth key math.oc networks optimization power quantization robots sensor smart smart buildings solution systems type