In addition to threatening human lives, the recent COVID-19 pandemic has also highlighted how misinformation is plaguing our online social networks. However, privacy and ethical concerns reduce data sharing by stakeholders, impeding data-driven misinformation detection. Current data encryption techniques—providing privacy guarantees on data—cannot be naively extended to inferring text inputs with Deep Learning (DL) models mainly due to inherent non-polynomial operations (which are not encryption-compatible), error introduced by approximate polynomial activations (because they are only valid for a limited range of input values), and error accumulated over stacked encrypted operations of DL classifiers. In this paper, we propose encrypted federated learning (EFL) framework for text-based misinformation detection as a (secure and privacy-aware cloud) service, where classifiers are securely trained in FL framework—which ensures the privacy of training data holders—and later inference is performed on homomorphically encrypted data—which ensures the privacy of clients’ data. We evaluate three classifier architectures—Logistic Regression (LR), Multilayer Perceptron (MLP), and the novel encryption-compatible self-attention network (SAN) proposed in this paper—on two publicly available text-based misinformation detection datasets. To reduce the error induced by polynomial activations, we, formally and empirically, show the efficacy of L₂ regularization while training the classifier. To regulate the error accumulated due to cascaded operations over encrypted data, we advocate the use of sigmoid activation with formal proofs and empirically validate our claims. In our case, by simply replacing ReLU activation with sigmoid, we were able to reduce the output error by 1750 times in the best case to 43.75 times in the worst case.