  Federated Learning (FL) is a decentralized machine-learning paradigm, in
which a global server iteratively averages the model parameters of local users
without accessing their data. User heterogeneity has imposed significant
challenges to FL, which can incur drifted global models that are slow to
converge. Knowledge Distillation has recently emerged to tackle this issue, by
refining the server model using aggregated knowledge from heterogeneous users,
other than directly averaging their model parameters. This approach, however,
depends on a proxy dataset, making it impractical unless such a prerequisite is
satisfied. Moreover, the ensemble knowledge is not fully utilized to guide
local model learning, which may in turn affect the quality of the aggregated
model. Inspired by the prior art, we propose a data-free knowledge
distillation} approach to address heterogeneous FL, where the server learns a
lightweight generator to ensemble user information in a data-free manner, which
is then broadcasted to users, regulating local training using the learned
knowledge as an inductive bias. Empirical studies powered by theoretical
implications show that, our approach facilitates FL with better generalization
performance using fewer communication rounds, compared with the
state-of-the-art.
