Federated Learning With Experience-Driven Model Migration in Heterogeneous Edge Networks

To approach the challenges of non-IID data and limited communication resource raised by the emerging federated learning (FL) in mobile edge computing (MEC), we propose an efficient framework, called
FedMigr
, which integrates a deep reinforcement learning (DRL) based model migration strategy into the pioneer FL algorithm
FedAvg
. According to the data distribution and resource budgets, our
FedMigr
will intelligently guide one client to forward its local model to another client after local updating, before directly sending the local models to the server for global aggregation as in
FedAvg
. Intuitively, migrating a local model from one client to another is equivalent to training the model over more data from different clients, alleviating the influence of non-IID issue. To this end, we propose an experience-driven method to make proper decisions for model migrations while satisfying the resource constraints. We also prove that
FedMigr
can help to reduce the parameter divergences between different local models and the global model from a theoretical perspective under the non-IID setting. Extensive experiments on three popular benchmark datasets demonstrate that
FedMigr
can achieve an average accuracy improvement of around 13%, and reduce bandwidth consumption for global communication by 42% on average, compared with the baselines.