Abstract: Constructing a novel model for the storage task allocation problem of federated learning client datasets,to ensure load balancing of decentralized cloud storage networks,shorten the storage data uploading and recovery time,and reduce the total client storage cost,a data storage task allocation algorithm——URGL_allo (allocation based on user requirements and global load) that considers client requirements and global load is proposed.In the node allocation phase,node resources such as global load,topological attributes,storage price and data recovery time concerned by clients are considered,and a new node ranking method is defined in conjunction with the law of gravity to select the best storage task allocation node.In the link allocation stage,the shortest path calculation is performed using Dijkstra’s algorithm for the client node as the center to other nodes in the network,and the path with the largest bandwidth value in the set of shortest paths between two nodes is selected for allocation.Simulation results show that the proposed algorithm reduces the load balancing index and the total client storage cost by 41.9% and 5%,respectively,compared with the random policy-based allocation algorithm (Random_allo),and the data recovery time is not much different from that of the link bandwidth-based greedy algorithm,both of which are stably maintained between (0,2],which is 1/20 of Random_allo.The combined performance of global load and service quality is better than that of the comparison algorithm.

Key words: Federated learning, Decentralized cloud storage, Storage task allocation, Global load, Client requirements, Node sequencing