  Federated learning (FL) is a distributed machine learning approach where
multiple clients collaboratively train a joint model without exchanging their
data. Despite FL's unprecedented success in data privacy-preserving, its
vulnerability to free-rider attacks has attracted increasing attention.
Existing defenses may be ineffective against highly camouflaged or high
percentages of free riders. To address these challenges, we reconsider the
defense from a novel perspective, i.e., model weight evolving
frequency.Empirically, we gain a novel insight that during the FL's training,
the model weight evolving frequency of free-riders and that of benign clients
are significantly different. Inspired by this insight, we propose a novel
defense method based on the model Weight Evolving Frequency, referred to as
WEF-Defense.Specifically, we first collect the weight evolving frequency
(defined as WEF-Matrix) during local training. For each client, it uploads the
local model's WEF-Matrix to the server together with its model weight for each
iteration. The server then separates free-riders from benign clients based on
the difference in the WEF-Matrix. Finally, the server uses a personalized
approach to provide different global models for corresponding clients.
Comprehensive experiments conducted on five datasets and five models
demonstrate that WEF-Defense achieves better defense effectiveness than the
state-of-the-art baselines.
