In Federated Learning (FL), a group of workers participate to build a global
model under the coordination of one node, the chief. Regarding the
cybersecurity of FL, some attacks aim at injecting the fabricated local model
updates into the system. Some defenses are based on malicious worker detection
and behavioral pattern analysis. In this context, without timely and dynamic
monitoring methods, the chief cannot detect and remove the malicious or
unreliable workers from the system. Our work emphasize the urgency to prepare
the federated learning process for monitoring and eventually behavioral pattern
analysis. We study the information inside the learning process in the early
stages of training, propose a monitoring process and evaluate the monitoring
period required. The aim is to analyse at what time is it appropriate to start
the detection algorithm in order to remove the malicious or unreliable workers
from the system and optimise the defense mechanism deployment. We tested our
strategy on a behavioral pattern analysis defense applied to the FL process of
different benchmark systems for text and image classification. Our results show
that the monitoring process lowers false positives and false negatives and
consequently increases system efficiency by enabling the distributed learning
system to achieve better performance in the early stage of training.

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