  As a promising learning paradigm integrating computation and communication,
federated learning (FL) proceeds the local training and the periodic sharing
from distributed clients. Due to the non-i.i.d. data distribution on clients,
FL model suffers from the gradient diversity, poor performance, bad
convergence, etc. In this work, we aim to tackle this key issue by adopting
importance sampling (IS) for local training. We propose importance sampling
federated learning (ISFL), an explicit framework with theoretical guarantees.
Firstly, we derive the convergence theorem of ISFL to involve the effects of
local importance sampling. Then, we formulate the problem of selecting optimal
IS weights and obtain the theoretical solutions. We also employ a water-filling
method to calculate the IS weights and develop the ISFL algorithms. The
experimental results on CIFAR-10 fit the proposed theorems well and verify that
ISFL reaps better performance, sampling efficiency, as well as explainability
on non-i.i.d. data. To the best of our knowledge, ISFL is the first non-i.i.d.
FL solution from the local sampling aspect which exhibits theoretical
compatibility with neural network models. Furthermore, as a local sampling
approach, ISFL can be easily migrated into other emerging FL frameworks.

Machine Learning;Artificial Intelligence;Distributed, Parallel, and Cluster Computing;Multiagent Systems