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Federated learning in network intrusion detection has one significant problem: Byzantine
attacks, in which compromised clients inject malicious updates. Existing Byzantineresilient strategies suffer significant degradation, with F1-scores decreasing by
approximately 96% when 30% of clients are malicious. This paper presents A3 (Adaptive
Attack-Aware Aggregation), which addresses this drawback through adaptive strategy
selection and dual aggregation modes. The strategy employs geometric filtering under
high-threat conditions and trust-based filtering under normal conditions. The design of A3
incorporates the use of triple-weighted scoring on the dimensions of diversity, confidence,
and trust, along with adaptive weight optimization and Byzantine detection. The CICIDS2017 data experiments indicate that A3 achieves an accuracy of 95.83% and an F1-
score of 95.90% in benign environments. The performance is still 94.99% accurate and
94.49% F1-score, only 0.84 percentage points lower than in the clean environment, in the
Byzantine environment of 30% malicious clients. The algorithm is consistent in many
forms of attacks and its accuracy deviates by a very small percentage of 0.71, but multiKrum exposes a disparity of 54.27. Study of comparative performance demonstrates that
A3 has higher performance than FedAvg (84.40%), Median (94.49%), and multi-Krum
(82.98%). The highest results are obtained with the golden ratio weighting (95.36%
accuracy) whereas confidence scoring is the most potent single element. It consists of a
geometricalally filtered Krum-style distance analysis, statistical Byzantine detection, round
2 at the start and momentum smoothing, an i value of 0.15. Blending validation can also
be used by utilizing a combination of scores of A3 (60 percent) and empirical check-up (40xvii
percent). The outcomes of multiple runs demonstrate very statistically significant outcome
(p < 0.001), which reveals that the systems federated to identify intrusions are stronger in
case with A3.