  Federated Learning (FL) is a distributed learning paradigm that enhances
users privacy by eliminating the need for clients to share raw, private data
with the server. Despite the success, recent studies expose the vulnerability
of FL to model inversion attacks, where adversaries reconstruct users private
data via eavesdropping on the shared gradient information. We hypothesize that
a key factor in the success of such attacks is the low entanglement among
gradients per data within the batch during stochastic optimization. This
creates a vulnerability that an adversary can exploit to reconstruct the
sensitive data. Building upon this insight, we present a simple, yet effective
defense strategy that obfuscates the gradients of the sensitive data with
concealed samples. To achieve this, we propose synthesizing concealed samples
to mimic the sensitive data at the gradient level while ensuring their visual
dissimilarity from the actual sensitive data. Compared to the previous art, our
empirical evaluations suggest that the proposed technique provides the
strongest protection while simultaneously maintaining the FL performance.
