Boosting federated learning in optical networks for edge computing

This invited talk will introduce several useful means to boost federated learning carried out over passive optical networks for edge computing. One way is to introduce bandwidth slicing that is able to reserve network resource dedicated to the learning task and can well address the issues brought by stragglers during the training process. Another way is to devise aggregation function carried out at intermedium network nodes capable of significantly reducing the amount of traffic to be exchanged for global training while not impacting the learning performance. Simulation results show that the FL training efficiency can be significantly improved while achieving the same level of learning accuracy. For the specific FL task implemented in the context of edge computing, the training time can be saved up to 36 % to achieve the maximum learning accuracy.