About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
UAI 2023
Conference paper
FLASH: Automating Federated Learning using CASH
Abstract
In this paper, we present FLASH, a framework which addresses for the first time the central AutoML problem of Combined Algorithm Selection and HyperParameter (HP) Optimization (CASH) in the context of Federated Learning (FL). To limit training cost, FLASH incrementally adapts the set of algorithms to train based on their projected loss rates, while supporting decentralized (federated) implementation of the embedded hyperparameter optimization (HPO), model selection and loss calculation problems. We provide a theoretical analysis of the training and validation loss under FLASH, and their tradeoff with the training cost measured as the data wasted in training sub-optimal algorithms. The bounds depend on the degree of dissimilarity between the datasets of the clients, a result of FL restriction that client datasets remain private. Through extensive experimental investigation on several datasets, we evaluate three variants of FLASH, and show that FLASH performs close to centralized CASH methods.