LLM watermarking has emerged as a promising approach for detecting downstream model misuse in centralized settings, but extending it to federated learning (FL) presents unique challenges. In particular, FL systems often rely on strong robust aggregators to defend against poisoning attacks. We empirically show that a malicious server can exploit these same aggregators as effective watermark-removal mechanisms during training, causing representative existing LLM watermarking methods to fail detection in the final model. To address this limitation, we formalize the problem as a constrained optimization task and propose CamoMark, an aggregator-agnostic watermarking scheme for embedding robust watermarks in FL. CamoMark optimizes watermarked updates to become inliers, maintaining bounded deviation from the distribution of clean updates, while preserving sufficient watermark signal for reliable detection. Evaluated on Qwen fine-tuned on the Alpaca dataset, CamoMark achieves a detection confidence of $p$-value $= 1.82\times10^{-2}$ using only 3.3% watermarked data. The watermark also remains robust to a range of post-training removal attacks, including continued fine-tuning on clean data, pruning, and model distillation. At higher watermark ratios, detection confidence improves substantially, reaching $p$-value $= 5.11\times10^{-11}$ at a 13.3% watermark ratio.

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