  Distributed fuzzy neural networks (DFNNs) have attracted increasing attention
recently due to their learning abilities in handling data uncertainties in
distributed scenarios. However, it is challenging for DFNNs to handle cases in
which the local data are non-independent and identically distributed (non-IID).
In this paper, we propose a federated fuzzy neural network (FedFNN) with
evolutionary rule learning (ERL) to cope with non-IID issues as well as data
uncertainties. The FedFNN maintains a global set of rules in a server and a
personalized subset of these rules for each local client. ERL is inspired by
the theory of biological evolution; it encourages rule variations while
activating superior rules and deactivating inferior rules for local clients
with non-IID data. Specifically, ERL consists of two stages in an iterative
procedure: a rule cooperation stage that updates global rules by aggregating
local rules based on their activation statuses and a rule evolution stage that
evolves the global rules and updates the activation statuses of the local
rules. This procedure improves both the generalization and personalization of
the FedFNN for dealing with non-IID issues and data uncertainties. Extensive
experiments conducted on a range of datasets demonstrate the superiority of the
FedFNN over state-of-the-art methods.

Machine Learning;Neural and Evolutionary Computing