Open Radio Access Network (O-RAN) improves radio access network flexibility through disaggregation, open interfaces, and programmable RAN Intelligent Controllers (RICs). However, the same openness and distribution increase the attack surface and make centralized intrusion detection difficult to scale across heterogeneous RAN sites. This paper presents a federated lightweight Transformer framework for privacy-preserving network-layer cyberattack detection in O-RAN. Each participating site trains a compact Transformer locally on temporally ordered network-flow records, while only model parameters are shared with a federated coordinator. To model realistic site heterogeneity, the client datasets are partitioned according to a Dirichlet distribution, resulting in non-independent and identically distributed (non-IID) local data. The local detector uses feature projection, positional encoding, multi-head self-attention, feed-forward layers, and temporal pooling to classify five network-layer attack categories: ICMP, Slowloris, SYN, TCP-ACK, and UDP. Experiments on the NetsLab 5G O-RAN intrusion-detection dataset show an overall accuracy of 94.89 % and a macro-F1 score of 81.09 %.

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