SYSTEMS AND METHODS FOR CALCULATING VALIDATION LOSS FOR MODELS IN DECENTRALIZED MACHINE LEARNING

摘要

Systems and methods are provided for calculating validation loss in a distributed machine learning network, where nodes train local instances of a machine learning model using local data maintained at those nodes. After each training iteration of the local instances of the machine learning model, each node may calculate a local validation loss value corresponding to the performance of the local instance of the machine learning model trained at each of the nodes. Those local validation loss values may be shared with an elected leader that can average all the local validation loss values, return a global validation loss value to the nodes. The nodes may then determine whether or not training of their local instance of the machine learning model should stop or continue.