FLAccShield: Federated Learning with Accuracy-Weighted Aggregation for DDoS Detection

5G network slicing enables flexible and efficient service deployment by maintaining logical isolation and facilitating tailored Quality of Service (QoS) and security policies. However, network slices increase network complexity and expand the attack surface, making them more susceptible to Distributed Denial-of-Service (DDoS) attacks. Due to the growing attack landscape, Machine Learning (ML)-based Intrusion Detection Systems (IDS) are becoming more popular over traditional IDS. Since network slices are used by different verticals, they often have strict privacy requirements. Conventional ML-based IDS are centralized in nature, combining all data on a central server, which fails to uphold slice-level privacy constraints. This paper proposes FLAccShield, a Federated Learning (FL)-based privacy-preserving IDS framework for DDoS detection in 5G networks through collaboration among multiple slices. The framework is rigorously evaluated for supervised and unsupervised settings for both Independent and Identically Distributed (IID) and non-IID datasets against a varied range of features. The experimental results demonstrate that FLAccShield achieves consistent and stable detection performance across varying feature set sizes and data distributions. In supervised settings with non-IID data, the most practically challenging scenario, FLAccShield outperforms FedAvg and FedProx by approximately 3% in F1 score while exhibiting substantially less performance degradation as feature count increases (7% decline versus 10-22% for baselines). In supervised IID settings, FLAccShield maintains uniform per-client performance where baselines show client-level deviation. In unsupervised anomaly detection, FLAccShield achieves comparable performance to FedAvg and FedProx (within 0.4 - 1%), while QFedAvg proves poorly suited to the autoencoder-based anomaly detection paradigm.