Abstract: Systems, devices, and methods for disclosed for federated learning in a network of nodes. The nodes include an aggregator node interconnected with a plurality of client nodes. Each client node performs local model training to generate locally trained model parameter values and the aggregator node aggregates the locally trained model parameter values to compute global model parameter values.

Abstract: Systems and methods for generating machine learning model output from an input data set is provided. The system includes a processor and a memory coupled to the processor. The memory may store processor-executable instructions that, when executed, configure the processor to: obtain a qualitative data set; determine a regularization threshold value based on the qualitative data set for regularizing the machine learning output; determine a quantitative feedback score for the input data set, wherein the quantitative feedback score includes a bias-detection indication value; determine an adjustment parameter based on the quantitative feedback score and the regularization threshold value; and update the machine learning model based on the determined adjustment parameter.