Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimization

arXiv:2305.13503v3 Announce Type: replace
Abstract: Federated learning (FL) has emerged as a key technique for distributed machine learning (ML). Most literature on FL has focused on ML model training for (i) a single task/model, with (ii) a synchronous scheme for updating model parameters, and (iii) a static data distribution setting across devices, which is often not realistic in practical wireless environments. To address this, we develop DMA-FL considering dynamic FL with multiple downstream tasks/models over an asynchronous model update architecture. …

abstract arxiv asynchronous cs.dc cs.lg data distributed dynamic federated learning iii key literature machine machine learning modeling networks optimization parameters theory training type wireless