Asynchronous Wireless Federated Learning with Probabilistic Client
  Selection
Jiarong Yang Yuan Liu Fangjiong Chen Wen Chen Changle Li
Federated learning (FL) is a promising distributed learning framework where
distributed clients collaboratively train a machine learning model coordinated
by a server. To tackle the stragglers issue in asynchronous FL, we consider
that each client keeps local updates and probabilistically transmits the local
model to the server at arbitrary times. We first derive the (approximate)
expression for the convergence rate based on the probabilistic client
selection. Then, an optimization problem is formulated to trade off the
convergence rate of asynchronous FL and mobile energy consumption by joint
probabilistic client selection and bandwidth allocation. We develop an
iterative algorithm to solve the non-convex problem globally optimally.
Experiments demonstrate the superiority of the proposed approach compared with
the traditional schemes.