A computer-implemented method, comprising: determining variables maintained by each of a plurality of Learning Machines, LMs, which are hosted on a plurality of Field Area Routers, FARs, in a network, wherein: the network is a Low-Power and Lossy Network, LLN, the learning machines are arranged to perform iterative learning processes, and the variables represent network properties and are sharable between the FARs, wherein shareable variables are variables that are not specific to a network for which a FAR is responsible; calculating a plurality of correlation values defining a correlation between the variables of different FARs; computing a cluster of FARs based on the plurality of correlation values, wherein the clustered FARs are associated with correlated variables; determining available network resources in the network; determining, based on the available network resources, a sharing rate r defining a rate at which the variables are shared by the clustered FARs; sending a message to a given FAR in the cluster specifying the list of variables to be shared, the list of FARs to share with and the rate r at which data is to be sent, and sharing, between the clustered FARs, their respective variables, the sharing comprising, at a given FAR: starting a timer equal to 1/ r ; on expiry of the timer, sending the variables specified in the list of variables to the FARs in the list of FARs; and on receipt of variables from another FAR, transmitting the variables to the learning machine of the given FAR for use in a next iteration of its learning process. determining variables maintained by each of a plurality of Learning Machines, LMs, which are hosted on a plurality of Field Area Routers, FARs, in a network, wherein: the network is a Low-Power and Lossy Network, LLN, the learning machines are arranged to perform iterative learning processes, and the variables represent network properties and are sharable between the FARs, wherein shareable variables are variables that are not specific to a network for which a FAR is responsible; the network is a Low-Power and Lossy Network, LLN, the learning machines are arranged to perform iterative learning processes, and the variables represent network properties and are sharable between the FARs, wherein shareable variables are variables that are not specific to a network for which a FAR is responsible; calculating a plurality of correlation values defining a correlation between the variables of different FARs; computing a cluster of FARs based on the plurality of correlation values, wherein the clustered FARs are associated with correlated variables; determining available network resources in the network; determining, based on the available network resources, a sharing rate r defining a rate at which the variables are shared by the clustered FARs; sending a message to a given FAR in the cluster specifying the list of variables to be shared, the list of FARs to share with and the rate r at which data is to be sent, and sharing, between the clustered FARs, their respective variables, the sharing comprising, at a given FAR: starting a timer equal to 1/ r ; on expiry of the timer, sending the variables specified in the list of variables to the FARs in the list of FARs; and on receipt of variables from another FAR, transmitting the variables to the learning machine of the given FAR for use in a next iteration of its learning process. starting a timer equal to 1/ r ; on expiry of the timer, sending the variables specified in the list of variables to the FARs in the list of FARs; and on receipt of variables from another FAR, transmitting the variables to the learning machine of the given FAR for use in a next iteration of its learning process. <EOS>