Abstract: Examples of ensemble knowledge transfer in collaborative learning include: receiving, at a primary node, from a plurality of remote nodes, a plurality of trained proxy machine learning (ML) models, wherein each proxy ML model is received from a different one of the plurality of remote nodes, and wherein each of the plurality of remote nodes is remote across a network from the primary node; training a primary ML model using the plurality of proxy ML models, wherein training the primary ML model comprises: for each of a plurality of training cases of a primary training dataset, weighting results from each of the proxy ML models based on at least a confidence of the respective proxy ML model regarding the training case.

Abstract: Disclosed is a method for receiving data that can reduce intersymbol interference (ISI) and interlink interference (ILI) occurring in a MIMO molecular communication system. An embodiment of the present invention provides a method for receiving data at a receiver in a MIMO molecular communication system, where the method includes: determining an enzyme inhibitor discharge stopping timepoint by using the distance between the antennas of the transmitter and the distance between the transmitter and the receiver; discharging an enzyme inhibitor, which is configured to deactivate an enzyme that is distributed around the receiver, and receiving a molecule transmitted from the transmitter; and stopping the discharge of the enzyme inhibitor according to the enzyme inhibitor discharge stopping timepoint, and where the enzyme is reactive to the molecule.