  Despite the promise of Federated Learning (FL) for privacy-preserving model
training on distributed data, it remains susceptible to backdoor attacks. These
attacks manipulate models by embedding triggers (specific input patterns) in
the training data, forcing misclassification as predefined classes during
deployment. Traditional single-trigger attacks and recent work on cooperative
multiple-trigger attacks, where clients collaborate, highlight limitations in
attack realism due to coordination requirements. We investigate a more alarming
scenario: non-cooperative multiple-trigger attacks. Here, independent
adversaries introduce distinct triggers targeting unique classes. These
parallel attacks exploit FL's decentralized nature, making detection difficult.
Our experiments demonstrate the alarming vulnerability of FL to such attacks,
where individual backdoors can be successfully learned without impacting the
main task. This research emphasizes the critical need for robust defenses
against diverse backdoor attacks in the evolving FL landscape. While our focus
is on empirical analysis, we believe it can guide backdoor research toward more
realistic settings, highlighting the crucial role of FL in building robust
defenses against diverse backdoor threats. The code is available at
\url{https://anonymous.4open.science/r/nba-980F/}.
