  Federated Learning, designed to address privacy concerns in learning models,
introduces a new distributed paradigm that safeguards data privacy but
differentiates the attack surface due to the server's inaccessibility to local
datasets and the change in protection objective--parameters' integrity.
Existing approaches, including robust aggregation algorithms, fail to
effectively filter out malicious clients, especially those with
non-Independently and Identically Distributed data. Furthermore, these
approaches often tackle non-IID data and poisoning attacks separately. To
address both challenges simultaneously, we present FedCC, a simple yet novel
algorithm. It leverages the Centered Kernel Alignment similarity of Penultimate
Layer Representations for clustering, allowing it to identify and filter out
malicious clients by selectively averaging chosen parameters, even in non-IID
data settings. Our extensive experiments demonstrate the effectiveness of FedCC
in mitigating untargeted model poisoning and backdoor attacks. FedCC reduces
the attack confidence to a consistent zero compared to existing outlier
detection-based and first-order statistics-based methods. Specifically, it
significantly minimizes the average degradation of global performance by
65.5\%. We believe that this new perspective of assessing learning models makes
it a valuable contribution to the field of FL model security and privacy. The
code will be made available upon paper acceptance.
