A mobile electronic device (110a-110n, 111a-111n, 112a-112n) comprising: a machine-readable medium to store instructions; one or more processors to execute the instructions stored on the machine-readable medium, the instructions to cause the mobile electronic device (110a-110n, 111a-111n, 112a-112n) to: receive a set of labeled data from a server; receive a unit of data (131[i]) from the server, the unit of data (131[i]) of a same type of data as the set of labeled data; determine a proposed label for the unit of data (131[i]) via a machine learning model (136a-136n, 137a-137n, 138a-138n) on the mobile electronic device (110a-110n, 111a-111n, 112a-112n), the machine learning model (136a-136n, 137a-137n, 138a-138n) to determine the proposed label for the unit of data (131[i]) based on the set of labeled data from the server and a set of unlabeled data associated with the mobile electronic device (110a-110n, 111a-111n, 112a-112n), wherein to determine the proposed label for the unit of data (131[i]) from the server includes to cluster the set of unlabeled data based on the set of labeled data from the server via the machine learning model to generate a set of clustered unlabeled data, compare the unit of data (131[i]) from the server with the set of clustered unlabeled data, and determine the proposed label for the unit of data (131[i]) from the server based on the comparison; encode the proposed label via a privacy algorithm to generate a privatized encoding of the proposed label; and transmit the privatized encoding of the proposed label to the server. a machine-readable medium to store instructions; one or more processors to execute the instructions stored on the machine-readable medium, the instructions to cause the mobile electronic device (110a-110n, 111a-111n, 112a-112n) to: receive a set of labeled data from a server; receive a unit of data (131[i]) from the server, the unit of data (131[i]) of a same type of data as the set of labeled data; determine a proposed label for the unit of data (131[i]) via a machine learning model (136a-136n, 137a-137n, 138a-138n) on the mobile electronic device (110a-110n, 111a-111n, 112a-112n), the machine learning model (136a-136n, 137a-137n, 138a-138n) to determine the proposed label for the unit of data (131[i]) based on the set of labeled data from the server and a set of unlabeled data associated with the mobile electronic device (110a-110n, 111a-111n, 112a-112n), wherein to determine the proposed label for the unit of data (131[i]) from the server includes to cluster the set of unlabeled data based on the set of labeled data from the server via the machine learning model to generate a set of clustered unlabeled data, compare the unit of data (131[i]) from the server with the set of clustered unlabeled data, and determine the proposed label for the unit of data (131[i]) from the server based on the comparison; encode the proposed label via a privacy algorithm to generate a privatized encoding of the proposed label; and transmit the privatized encoding of the proposed label to the server. receive a set of labeled data from a server; receive a unit of data (131[i]) from the server, the unit of data (131[i]) of a same type of data as the set of labeled data; determine a proposed label for the unit of data (131[i]) via a machine learning model (136a-136n, 137a-137n, 138a-138n) on the mobile electronic device (110a-110n, 111a-111n, 112a-112n), the machine learning model (136a-136n, 137a-137n, 138a-138n) to determine the proposed label for the unit of data (131[i]) based on the set of labeled data from the server and a set of unlabeled data associated with the mobile electronic device (110a-110n, 111a-111n, 112a-112n), wherein to determine the proposed label for the unit of data (131[i]) from the server includes to cluster the set of unlabeled data based on the set of labeled data from the server via the machine learning model to generate a set of clustered unlabeled data, compare the unit of data (131[i]) from the server with the set of clustered unlabeled data, and determine the proposed label for the unit of data (131[i]) from the server based on the comparison; encode the proposed label via a privacy algorithm to generate a privatized encoding of the proposed label; and transmit the privatized encoding of the proposed label to the server.