When information freshness meets service latency in federated learning : a task-aware incentive scheme for smart industries

Abstract

For several industrial applications, a sole data owner may lack sufficient training samples to train effective machine learning based models. As such, we propose a Federated Learning (FL) based approach to promote privacy-preserving collaborative machine learning for applications in smart industries. In our system model, a model owner initiates an FL task involving a group of workers, i.e., data owners, to perform model training on their locally stored data before transmitting the model updates for aggregation. There exists a tradeoff between service latency, i.e., the time taken for the training request to be completed, and Age of Information (AoI), i.e., the time elapsed between data aggregation from the deployed IIoT devices to completion of the FL based training. On one hand, if the data is collected only upon the model owner's request, the AoI is low. On the other hand, the service latency incurred is more significant. Furthermore, given that different training tasks may have varying AoI requirements, we propose a contract-theoretic task-aware incentive scheme that can be calibrated based on the weighted preferences of the model owner towards AoI and service latency. Performance evaluation validates the incentive compatibility of our contract amid information asymmetry, and shows the flexibility of our proposed scheme towards satisfying varying preferences of AoI and service latency.