The invention discloses a safe and efficient distribution and incremental updating method and
system for an AI
large model, and belongs to the technical field of
artificial intelligence,
cloud computing and
data security. The method comprises the following steps: firstly, performing quantification or
pruning compression on an original large-scale
machine learning model to reduce the volume of data to be distributed; and then encrypting the compressed model and storing the compressed model to distributed nodes in a fragmented manner. Through an intelligent scheduling
server, on the basis of the hardware capability and
network topology of edge nodes, a proper fragment downloading source is distributed to the edge nodes, and parallel high-speed downloading and local recombination loading are achieved. For model version updating, the central
server calculates binary
system difference between a new version and an old version and generates an increment updating
package, only the increment
package is distributed to an
edge node, the node locally applies updating to reconstruct a new version model, and updating flow and
delay are greatly reduced. According to the method,
high bandwidth utilization rate,
low transmission overhead, full-link security protection and intelligent
resource scheduling of the hundred-GB-to-TB-level
large model in the cross-region distribution and
iteration process are realized, and the efficiency and security of model deployment and updating are remarkably improved.