Abstract: A method for distributing an equity reward for federated learning based on an equity theory includes the following steps: applying Adams' equity theory to federated learning, analyzing, by a participant, all factors invested in a federated task comprehensively, then giving an expected reward for this task, calculating, by the task publisher, the reputation of the participant; participating, by the participant, in each round of a training task using a local data to evaluate data contribution, model contribution, and a waiting-time allowance of the participant, then combining contribution results of the three factors to evaluate the contribution of the participant; after a global model converges, dynamically adjusting weights of the three factors according to an objective function of the equity reward, with a goal that an actual reward of the participant is as close as possible to the expected reward, and obtaining and distributing the actual reward of the participant.

Abstract: The present disclosure may provide a method for route planning. The method may include obtaining a start location and a destination of a target vehicle. The method may also include obtaining a map of a target region including the start location and the destination. The map may include node information of each of a plurality of nodes. Further, the method may include obtaining motion status information associated with one or more vehicles other than the target vehicle in the target region. The method may further include determining a target route of the target vehicle based at least in part on the start location, the destination, the motion status information associated with the one or more vehicles, and the map.