A system of decentralized machine learning, ML, comprising: a self-healing computer node of a blockchain network (110, 200) comprising a plurality of computing nodes, characterized in that the self-healing computer node recovering from a fault condition within the blockchain network (110, 200) and being programmed to: generate a first blockchain transaction (19, 208) comprising an indication that the self-healing computer node is out-of-sync with a most recent iteration of training a machine-learned model and not ready to contribute to training the machine-learned model, wherein the first blockchain transaction (19, 208) is to be added to a distributed ledger of the blockchain network (110, 200) and informs the plurality of computing nodes that the self-healing computer node is not ready to participate in a subsequent iteration of training the machine-learned model; obtain a global ML state from the distributed ledger, wherein the global ML state is based on collaborative learning from the plurality of computing nodes; compare the obtained global ML state with a local ML state at the self-healing computer node to determine whether the local ML state is consistent with global ML state, wherein the local ML state is based on learning performed locally at the self-healing computer node; upon determining that the local ML state is not consistent with the global ML state, trigger a corrective action using the blockchain network (110, 200) to recover the local ML state to be consistent with the global ML state and achieve synchronization with the blockchain network (110, 200); generate a second blockchain transaction (19, 208) comprising an indication that the self-healing computer node is in sync with the most recent iteration and ready to contribute to training a machine-learned model, wherein the second blockchain transaction (19, 208) is to be added to the distributed ledger and informs the plurality of computing nodes that the self-healing computer node is ready to participate in a subsequent iteration of training the machine-learned model; and re-enrolling the self-healing computer node with the blockchain network (110, 200) to participate in the subsequent iteration of training the machine-learned model. generate a first blockchain transaction (19, 208) comprising an indication that the self-healing computer node is out-of-sync with a most recent iteration of training a machine-learned model and not ready to contribute to training the machine-learned model, wherein the first blockchain transaction (19, 208) is to be added to a distributed ledger of the blockchain network (110, 200) and informs the plurality of computing nodes that the self-healing computer node is not ready to participate in a subsequent iteration of training the machine-learned model; obtain a global ML state from the distributed ledger, wherein the global ML state is based on collaborative learning from the plurality of computing nodes; compare the obtained global ML state with a local ML state at the self-healing computer node to determine whether the local ML state is consistent with global ML state, wherein the local ML state is based on learning performed locally at the self-healing computer node; upon determining that the local ML state is not consistent with the global ML state, trigger a corrective action using the blockchain network (110, 200) to recover the local ML state to be consistent with the global ML state and achieve synchronization with the blockchain network (110, 200); generate a second blockchain transaction (19, 208) comprising an indication that the self-healing computer node is in sync with the most recent iteration and ready to contribute to training a machine-learned model, wherein the second blockchain transaction (19, 208) is to be added to the distributed ledger and informs the plurality of computing nodes that the self-healing computer node is ready to participate in a subsequent iteration of training the machine-learned model; and re-enrolling the self-healing computer node with the blockchain network (110, 200) to participate in the subsequent iteration of training the machine-learned model.