Orchestrating Fail-Safe, Black-Box Models Within Federated Learning Scenarios

Federated Learning (FL) stands out in the realm of collaborative learning that ensures data privacy for each client involved in the federation. Despite showing astounding potential, it inherently brings challenges that are often difficult to address and make it hardly applicable in industrial scenarios. First, all clients must agree on the same algorithm to be trained locally to enable global averaging, limiting autonomy. Second, clients are required to disclose insights of their models (i.e., weights or gradients) to create the global model upon averaging and aggregating techniques, threatening privacy. Third, applications of FL in critical systems where each component has to be trusted are not solid enough at best. This paper designs Trustworthy, Black-Box FL (TBB-FL) software architectures that allow clients to locally train any algorithm they want to solve a specific task. Only executables of local models are sent to the server and herein treated as black-boxes to create the global model as an adjudication of clients' opinions. Moreover, local and global models are self-checking, software components that quantify confidence in a prediction to suspect prediction errors, ultimately rejecting outputs that cannot be trusted. We validate this approach through classification experiments on both image and tabular datasets using the Flower framework, comparing TBB-FL against traditional FL and against individual local models. TBB-FL heavily reduces misclassifications compared to traditional FL, with minimal accuracy drop, and has better classification performance than local models alone.